基于支持向量机的我国家电企业创新能力评价指标体系研究
|
摘要
随着科学技术的日新月异和经济全球化步伐的加快,企业创新能力成为发展中国家调整经济结构和提高国家竞争力的中心环节,日益受到发展中国家的重视。当前,我国正处在全面建设小康社会、走新型工业化道路、建立创新型国家的关键时期,加快企业的创新步伐,提升企业的创新能力已经引起政府、产业界和理论界的高度重视。对企业创新能力的测度与评价是人们认识与把握这种创造性活动的本质与规律、系统总结创新经验的主要手段,对于正确制订创新政策、提高企业创新水平、不断提高企业核心能力具有重要意义。
本文在介绍了国内外学者关于创新能力研究成果和我国家电行业的发展现状后,在大量实地调研和访谈并借鉴国内外学者关于创新能力研究成果的基础上,围绕客观、公正对家电企业的创新能力进行测度和评价,以促进家电企业创新能力的提升这一主线。根据我国家行业的实际情况,从系统观点出发,运用企业能力、技术创新、管理创新、文化创新、系统工程和运筹学最优化等方面的理论,从创新硬实力、创新软实力和创新执行力的角度,构建了家电企业的创新能力评价模型。以家电企业的实际经营数据为基础,采用无监督支持向量机构建算法对指标进行特征选择,建立了客观的评价指标体系并赋予指标权重。
本文的研究内容和创新点包括:
1、本文总结了企业创新能力的定义:企业创新能力是由观念创新引导的,技术创新、管理创新、市场创新和文化创新组成的。不同种类的创新都是有层次的,各层次的具体创新对企业的功能和作用是不同的,高层次的创新给企业带来的效益或领先优势要远远大于低层次的创新。根据经验的总结,本文得出技术创新中的专利和国际标准,以及文化创新中的创新文化处于最高层,它们所形成的创新壁垒或领先年限最大。
2、本文在借鉴前人对创新能力的研究成果的基础上,通过企业创新能力的性质、组成、层次和作用揭示了企业创新能力的本质和内涵。首次定性和定量总结了各种创新所形成的创新壁垒,即创新所取得的领先优势,为企业创新能力的评价打下了坚实的基础。对现有的创新能力评价指标体系和评价方法进行了分析和比较。
3、本文对现行主要创新能力评价方法进行了研究比较,分析了各种方法的特点及用于评价企业创新能力的局限性。同时,对前人所建立的创新能力评价指标体系进行了比较研究,分析了各种评价指标体系的优点和局限性,为本文评价模型的建立和指标体系方法的选择打下了坚实的基础。
4、本文系统回顾了我国家电行业的发展历史,深入分析了我国家电行业的现状和行业特征,总结了我国家电行业特有的创新路径,指出我国家电企业创新能力薄弱的原因。根据我国家电企业的特征,创新能力的功能和作用,首次建立了“创新投入层”、“企业层”和“创新产出层”三层创新能力评价模型,该模型通过“创新硬实力”、“创新软实力”和“创新执行力”综合评价家电企业的创新能力。该评价模型是企业创新能力评价的一次创新,对同类评价有较大的借鉴意义。
5、为了消除创新能力评价中人为因素的干扰,本文首次将无监督支持向量机引入创新能力评价这一研究领域。在借鉴前人研究成果的基础上,本文对无监督支持向量机进行算法改进,建立了FS-primal-SVM模型。该模型直接针对家电企业的实际经营数据进行评价,无需像传统评价方法那样建立评判集,因而最大限度地消除了人为因素的干扰。运用该模型对家电企业的实际经营数据进行特征选择,去除数据中多余的指标,同时给选中的指标赋权重,从而最终计算出每个企业的创新能力指数。
6、在实际中,任何数据的获得都可能带有试验或者统计误差,因此对扰动数据的处理是一个重要问题。本文针对家电企业的实际经营数据中可能存在的误差,以无监督标准支持向量分类机为基础,创建了数据在多面体扰动的情况下鲁棒无监督标准支持向量分类机,建立了FS-Robust-primal-SVM模型,并由此模型对数据进行处理得到稳健的家电企业创新能力的评价指标体系和综合评价函数。
With the continuous development of science and technology and the acceleration of the world economic globalization, the innovation capability of enterprise receive great attention in developing country and gradually becomes the central link in the adjustment of economic structure and the elevation of country competence. Nowadays, our country just stay in the crucial period of comprehensive constructing well-off society, taking the new-type industrialized road and establishing the innovative country, therefore, accelerating the innovation step of enterprise, advancing innovation capacity of enterprise have absorbed high attention in government, industry and theory fields. The measurement and evaluation of enterprise innovation capability is the main measure for people to understand and grasp the essence and rule of thess innovation activities, and to systemically summarize innovation experience. It provides important significance in drawing innovation policy, elevating enterprise innovation level, and continuously advancing enterprise competence.
Based on summarizing study achievement of related Innovation capability and introducing the develop situation of our household appliances industry, through plentiful survey on the spot and interview, this paper encloses the measurement and evaluation of household appliances enterprise innovation capability on object and justice in order to advance the innovation capability in household appliances enterprises. Following the fact of our household appliances industry, setting out with system view, using the theory of enterprise ability, technology innovation, management innovation, culture innovation, system engineering and optimization, establish the evaluation system model of household appliances enterprise innovation capability with hard strength, soft strength and executive strength. Based on the practice business data of house electronic appliance industry, this paper applies unsupervised support vector machines for feature selection, so as to construct objective index system with weights and evaluation function.
The main study content and innovative points of the dissertation are as follows:
1、Summarizing the concept of enterprise innovation capability: guided by concept innovation, enterprise innovation capability consist with technology innovation, management innovation, marketing innovation and culture innovation. Each innovation have it's arrangement, and each layer have different function and effect to enterprise. High layer innovation provide far more benefits and advantages than low layer. Based on the experience, this dissertation educe that patent and national standard in technology innovation, innovation culture present the tiptop, which providing the highest innovation barrier and longest leading period.
2、Based on summarizing previous study achievement of related Innovation capability, this paper disclose the essence and intension of innovation capability through analyzing it's character, constitution, arrangement and function. First qualitatively andquantitatively summarize the innovation barrier——pioneering advantages, induced byall kinds of innovation. This provides stability groundwork for evaluation enterprise innovation capability. Then analyze and compare the existing evaluation indexes and evaluation measures of innovation capability.
3、Through comparative research, this paper analyzes the character and limitation of all kinds of existing evaluation measure about innovation capability. Meanwhile, analyzes the virtue and limitation about evaluation index of innovation capability funded by previous scholar. This affords massiness base on establishing evaluation model and choosing evaluation measure.
4、This paper systematically reviews development history of our household appliances industry, thoroughly analyzes actuality and character, summarizes innovation paths, and indicates the reason of unsubstantial innovation capability. Accounting the character of our household appliances industry and function of innovation capability, this paper firstly establish innovation capability model based on "innovation investment layer", "enterprise layer" and "innovation outcome layer", through "innovation hard ability", "innovation soft ability" and "innovation executive ability" to comprehensively evaluate. This evaluation model is an innovation on innovation capability evaluation,providing some reference meaning on other evaluation fields.
5、In order to eliminate the artificial interference on evaluation innovationcapability, this paper first inducts unsupervised support vector machines into this study field. Based on the previous study achievement, this paper ameliorates arithmetic, and funds FS-primal-SVM model. This model evaluates directly on the practice business data of house electronic appliance enterprise, therefore eliminates the artificial interference farthest since it need no judgement sets like tradition evaluation measure. Apply this model for feature selection, wipe off redundant targets, endow target weights, and figure out innovation capability exponent of each enterprise.
6、In fact, every data may take statistical discrepancy or trail error, therefore it's a main matter to treat noisy data. Focusing on the probability discrepancy of practice business data, based on unsupervised support vector machines, this paper establish robust unsupervised support vector machines under polyhedral disturbance, the FS-Robust-primal-SVM model. Through this model to process data, acquiredovish innovation evaluation index and comprehensive evaluation function.
本文在介绍了国内外学者关于创新能力研究成果和我国家电行业的发展现状后,在大量实地调研和访谈并借鉴国内外学者关于创新能力研究成果的基础上,围绕客观、公正对家电企业的创新能力进行测度和评价,以促进家电企业创新能力的提升这一主线。根据我国家行业的实际情况,从系统观点出发,运用企业能力、技术创新、管理创新、文化创新、系统工程和运筹学最优化等方面的理论,从创新硬实力、创新软实力和创新执行力的角度,构建了家电企业的创新能力评价模型。以家电企业的实际经营数据为基础,采用无监督支持向量机构建算法对指标进行特征选择,建立了客观的评价指标体系并赋予指标权重。
本文的研究内容和创新点包括:
1、本文总结了企业创新能力的定义:企业创新能力是由观念创新引导的,技术创新、管理创新、市场创新和文化创新组成的。不同种类的创新都是有层次的,各层次的具体创新对企业的功能和作用是不同的,高层次的创新给企业带来的效益或领先优势要远远大于低层次的创新。根据经验的总结,本文得出技术创新中的专利和国际标准,以及文化创新中的创新文化处于最高层,它们所形成的创新壁垒或领先年限最大。
2、本文在借鉴前人对创新能力的研究成果的基础上,通过企业创新能力的性质、组成、层次和作用揭示了企业创新能力的本质和内涵。首次定性和定量总结了各种创新所形成的创新壁垒,即创新所取得的领先优势,为企业创新能力的评价打下了坚实的基础。对现有的创新能力评价指标体系和评价方法进行了分析和比较。
3、本文对现行主要创新能力评价方法进行了研究比较,分析了各种方法的特点及用于评价企业创新能力的局限性。同时,对前人所建立的创新能力评价指标体系进行了比较研究,分析了各种评价指标体系的优点和局限性,为本文评价模型的建立和指标体系方法的选择打下了坚实的基础。
4、本文系统回顾了我国家电行业的发展历史,深入分析了我国家电行业的现状和行业特征,总结了我国家电行业特有的创新路径,指出我国家电企业创新能力薄弱的原因。根据我国家电企业的特征,创新能力的功能和作用,首次建立了“创新投入层”、“企业层”和“创新产出层”三层创新能力评价模型,该模型通过“创新硬实力”、“创新软实力”和“创新执行力”综合评价家电企业的创新能力。该评价模型是企业创新能力评价的一次创新,对同类评价有较大的借鉴意义。
5、为了消除创新能力评价中人为因素的干扰,本文首次将无监督支持向量机引入创新能力评价这一研究领域。在借鉴前人研究成果的基础上,本文对无监督支持向量机进行算法改进,建立了FS-primal-SVM模型。该模型直接针对家电企业的实际经营数据进行评价,无需像传统评价方法那样建立评判集,因而最大限度地消除了人为因素的干扰。运用该模型对家电企业的实际经营数据进行特征选择,去除数据中多余的指标,同时给选中的指标赋权重,从而最终计算出每个企业的创新能力指数。
6、在实际中,任何数据的获得都可能带有试验或者统计误差,因此对扰动数据的处理是一个重要问题。本文针对家电企业的实际经营数据中可能存在的误差,以无监督标准支持向量分类机为基础,创建了数据在多面体扰动的情况下鲁棒无监督标准支持向量分类机,建立了FS-Robust-primal-SVM模型,并由此模型对数据进行处理得到稳健的家电企业创新能力的评价指标体系和综合评价函数。
With the continuous development of science and technology and the acceleration of the world economic globalization, the innovation capability of enterprise receive great attention in developing country and gradually becomes the central link in the adjustment of economic structure and the elevation of country competence. Nowadays, our country just stay in the crucial period of comprehensive constructing well-off society, taking the new-type industrialized road and establishing the innovative country, therefore, accelerating the innovation step of enterprise, advancing innovation capacity of enterprise have absorbed high attention in government, industry and theory fields. The measurement and evaluation of enterprise innovation capability is the main measure for people to understand and grasp the essence and rule of thess innovation activities, and to systemically summarize innovation experience. It provides important significance in drawing innovation policy, elevating enterprise innovation level, and continuously advancing enterprise competence.
Based on summarizing study achievement of related Innovation capability and introducing the develop situation of our household appliances industry, through plentiful survey on the spot and interview, this paper encloses the measurement and evaluation of household appliances enterprise innovation capability on object and justice in order to advance the innovation capability in household appliances enterprises. Following the fact of our household appliances industry, setting out with system view, using the theory of enterprise ability, technology innovation, management innovation, culture innovation, system engineering and optimization, establish the evaluation system model of household appliances enterprise innovation capability with hard strength, soft strength and executive strength. Based on the practice business data of house electronic appliance industry, this paper applies unsupervised support vector machines for feature selection, so as to construct objective index system with weights and evaluation function.
The main study content and innovative points of the dissertation are as follows:
1、Summarizing the concept of enterprise innovation capability: guided by concept innovation, enterprise innovation capability consist with technology innovation, management innovation, marketing innovation and culture innovation. Each innovation have it's arrangement, and each layer have different function and effect to enterprise. High layer innovation provide far more benefits and advantages than low layer. Based on the experience, this dissertation educe that patent and national standard in technology innovation, innovation culture present the tiptop, which providing the highest innovation barrier and longest leading period.
2、Based on summarizing previous study achievement of related Innovation capability, this paper disclose the essence and intension of innovation capability through analyzing it's character, constitution, arrangement and function. First qualitatively andquantitatively summarize the innovation barrier——pioneering advantages, induced byall kinds of innovation. This provides stability groundwork for evaluation enterprise innovation capability. Then analyze and compare the existing evaluation indexes and evaluation measures of innovation capability.
3、Through comparative research, this paper analyzes the character and limitation of all kinds of existing evaluation measure about innovation capability. Meanwhile, analyzes the virtue and limitation about evaluation index of innovation capability funded by previous scholar. This affords massiness base on establishing evaluation model and choosing evaluation measure.
4、This paper systematically reviews development history of our household appliances industry, thoroughly analyzes actuality and character, summarizes innovation paths, and indicates the reason of unsubstantial innovation capability. Accounting the character of our household appliances industry and function of innovation capability, this paper firstly establish innovation capability model based on "innovation investment layer", "enterprise layer" and "innovation outcome layer", through "innovation hard ability", "innovation soft ability" and "innovation executive ability" to comprehensively evaluate. This evaluation model is an innovation on innovation capability evaluation,providing some reference meaning on other evaluation fields.
5、In order to eliminate the artificial interference on evaluation innovationcapability, this paper first inducts unsupervised support vector machines into this study field. Based on the previous study achievement, this paper ameliorates arithmetic, and funds FS-primal-SVM model. This model evaluates directly on the practice business data of house electronic appliance enterprise, therefore eliminates the artificial interference farthest since it need no judgement sets like tradition evaluation measure. Apply this model for feature selection, wipe off redundant targets, endow target weights, and figure out innovation capability exponent of each enterprise.
6、In fact, every data may take statistical discrepancy or trail error, therefore it's a main matter to treat noisy data. Focusing on the probability discrepancy of practice business data, based on unsupervised support vector machines, this paper establish robust unsupervised support vector machines under polyhedral disturbance, the FS-Robust-primal-SVM model. Through this model to process data, acquiredovish innovation evaluation index and comprehensive evaluation function.
引文
[1]2007年中国白色家电产业发展研究年度报告,赛迪顾问股份有限公司,2007年,5月。
[2]2007年中国冰箱行业研究报告,中华商务网,2008年,5月。
[3]2007年电冰箱/冷柜/冰箱压缩机研究发展报告,中国家用电器协会,2008年,3月。
[4]2007年全球冰箱(冰柜)生产布局及市场分布研究报告,中华商务网,2008年,4月。
[5]2007年度中国中央空调行业发展报告,艾肯空调制冷网,2008年,2月。
[6]2007——2008冷年全球家用空调行业年度研究报告,中华商务网,2007年,10月。
[7]2007——2008冷年中国家用空调行业年度研究报告,中华商务网,2007年,9月。
[8]2007——2008冷年中国家用空调行业年度研究报告
[9]2007电冰箱/冷柜/冰箱压缩机研究发展报告
[10]2008年中国白色家电产业发展研究年度报告,赛迪顾问股份有限公司,2008年,5月。
[11]2008年中国家用电器发展研究报告,中国家用电器协会,2008年,3月。
[12]2008年第一季度中国洗衣机市场研究报告,互联网消费调研中心,2008年,4月。
[13]2008年度中国空调行业白皮书,苏宁电器,2008年,5月。
[14]艾米顿[美]:知识经济的创新战略—智慧的觉醒[M],周金英译,北京:新华出版社1998年版,第87-88页。
[15]白俊红,江可申,李婧,林雷芳,企业技术创新能力测度与评价的因子分析模型及其应用,中国软科学,2008年3期。
[16]察志敏等,我国工业企业技术创新能力评价方法及实证研究[J]。统计研究,2004,(3):12-16。
[17]陈彬,洪家荣等,最优特征子集选择问题,计算机学报,2(1997),133-138.
[18]陈劲,童亮等,复杂产品系统创新评估指标体系研究[J]。研究与发展管理,2003:60-65。
[19]陈晓慧,企业技术创新能力的模糊综合[J],科技进步与对策,2002,19,(5):127-129。
[20]曹崇延,王淮学,企业技术创新能力评价指标体系研究。预测,1998(2):66-68。
[21]董岗,傅铅生,关于企业创新能力的评价模型研究:《商业研究》,2004年9期,33-36页。
[22]邓乃扬,田英杰.数据挖掘中的最优化方法—支持向量机.北京:科学出版社.2004.
[23]杜栋,企业技术创新评价的DEA方法[J]。系统工程理论方法应用,2001,10(1):82-84。
[24]范柏乃,城市技术创新透视[M],北京:机械工业出版社,2003 12。
[25]杜海涛,家电市场未饱和的依据何在[N],城乡机电信息报,2002.12.4。
[26]傅家骥,技术创新学。北京:清华大学出版社,1998:37-42,314-333。
[27]傅家骥,技术创新——中国企业发展之路。北京:企业管理出版社,1992:81-164。
[28]傅家骥:《技术经济学》,北京:清华大学出版社,1998 P2-3。
[29]郭璇,杨晓元,刘佳,韩鹏,基于遗传算法和一类SVM的隐秘图像检测方案,计算机工程与应用,43(20),2007,37-39+55.
[30]何传启,张风:知识创新——竞争新焦点[M],北京:经济管理出版社2001年版。
[31]何玉梅,龚灏,黄晴,四川省工业企业自主创新能力评测体系研究。《科技进步与对策》,2008年25卷5期。
[32]贾沛,特征选择技术研究,华中科技大学,2003年.
[33]姜炳鳞,谢延宇,企业技术创新能力评价指标体系及其多级模糊评价方法[J]。商业 研究,2004,18(502):77-79。
[34]江兵,企业技术创新系统运行机制与评价研究。合肥工业大学博士论文,2007年。
[35]蒋琴儿,曹艳春.绿色贸易壁垒对我国家电行业的影响及对策.企业活力,2004(5):15-17。
[36]韩景元,庞更新等,企业技术创新能力评价的理论、指标和方法[J],河北科技大学学报,2002,23,(3):90-94。
[37]韩立达,四川长虹打造产业航母的实证分析及战略构想。软科学,2003(4):17。
[38]何传启,张风:知识创新——竞争新焦点[M],北京:经济管理出版社2001年版。
[39]胡恩华,企业技术创新能力指标体系的构建及综合评价,科研管理。2001(4):79-84。
[40]胡小锋,赵辉.图像处理与识别应用案例精选(Visual C++/Matlab),人民邮电出版社,2004.9.
[41]胡志坚:国家创新系统——理论分析与国际比较[M],北京:社会科学文献出版社2000年版,第7页。
[42]科兹洛夫斯基:《结构主义及其反辩证法的性质》,载《国外社会科学》,1979(2)。
[43]李国训,中国家电企业跨国经营战略研究。武汉大学,2003。
[44]李金明,企业创新能力的分析模型,《东北大学学报》,2001年第4期。
[45]李金明,基于知识基础的企业创新能力研究。上海交通大学博士论文,2001年。
[46]李良成,中国家电品牌产品进入发达国家市场的四种商业模式。企业经济,2004(10):11-13。
[47]李敏强,寇纪淞,戴林,示例学习与特征选择的规划模型方法,系统工程学报,2(2006),163.167.
[48]李庆东,技术创新能力评价指标体系与评价方法研究。《现代情报》,2005年9期。
[49]李媛,企业技术创新的多层次分析。东北大学博士论文,2006年。
[50]李云,特征选择算法及其在基于内容图像检索中的应用研究,重庆大学博士学位论文,2005年.
[51]梁静国,姜金贵,企业创新能力的BP神经网络评价研究[J]。《物流科技》2004,11,79-81。
[52]廖春良,冯宗宪,程发新,中国家电产业国际价格竞争力分析。价格理论与实践,2004,3。
[53]凌锦江,陈兆乾,周志华,基于特征选择的神经网络集成方法,复旦大学学报,5(2004),685-688.
[54]刘凤朝等,基于集对分析法的区域自主创新能力评价研究[J]。中国软科学,2005(11):83-106.
[55]刘海月,何燕,论中国家电企业核心能力之培育[[J]。企业经济,2005,4:106-108。
[56]刘红燕,曹艺,21世纪中国家电业国际化发展战略[J]。经济师,2004,5:44-45。
[57]刘素华,候惠芳,李小霞,基于遗传算法和模拟退火算法的特征选择方法,计算机工程,16(2005),157-159.
[58]刘文军,李洪兴等,一种求粗糙集中最小属性约简的新方法,北京师范大学学报,1(2004),8-12.
[59]刘耀,黄新建,张滨松,许智宏,创新型企业创新能力评价指标体系研究。《南昌大学学报(人文社会科学版)》,2008年01期。
[60]刘勇国,李学明,基于遗传算法的特征子集选择,计算机工程,6(2003),19-20.
[61]柳卸林,中国创新管理前沿(第一辑),北京:北京理工大学出版社,2004年。
[62]路风:《动态企业理论的发展》,《国际经济评论》,2000年9-10,P46-510。
[63]卢怀宝等,企业技术创新能力的二次相对评价法[J],大庆石油学院学报,2002,26,(1):90-93。
[64]栾大龙 铉一民 姚彬 赵焕丛,基于粗糙集——主成分分析的企业创新能力评价实证研究,《计算机工程与应用》,2007年43卷4期,207-209页。
[65]马胜杰,企业技术创新能力及其评价指标体系,数量经济技术经济研究。2002(12):5-8。
[66]马贤娣,庄宇,安会刚,基于偏好型DEA的企业技术创新能力评价,《工业工程》,2007年10卷6期。
[67]马歇尔,经济原理,商务印书馆,北京,1997,P257。
[68]马歇尔,A:《经济学原理》(1920),朱志泰,译,商务印书馆,1981。
[69]尼古莱.J.福斯、克里斯蒂安.克努森:《企业万能:面向企业能力理论》,大连:东北财经大学出版社,1998年版,P28-29。
[70]乔立岩,彭喜元,马云彤,基于遗传算法和支持向量机的特征子集选择方法,20(2006),1-4.
[71]任荣伟,赵盈盈,“微笑曲线效应”下的公司海外并购式内创业战略整合——以TCL 的海外并购式内创业活动的胜败为例。现代管理科学,2007年10期。
[72]邵兴东,中国家电企业国际化战略研究—海尔、海信、TCL的国际化比较。青岛,中国海洋大学,2003。
[73]斯蒂芬,M,夏彼洛:《永续创新》(中文),北京:电子工业出版社,2003年1月,P19。
[74]石奇,论企业创新能力的构成与促进,《南京经济学院学报》,2002年第2期。
[75]史晓燕,企业技术创新能力指标体系设置及综合评价。陕西经贸学院学报,1999(4):27-30。
[76]宋河发,穆荣平等,自主创新及创新自主性测度研究[J]。中国软科学,2006,(6):60-66。
[77]宋志红,企业创新能力来源的是正研究。对外经济贸易大学博士论文,2006年。
[78]舒辉,论企业创新能力的基本结构及培育途径,《工业技术经济》,2003年第2期。
[79]苏越良,罗剑宏,企业技术创新能力的灰色关联分析,《中南工业大学学报:社会科学版》,2002年8卷2期,120-122。
[80]孙冰,李柏洲,企业技术创新动力的评价指标体系。《改革》,2005.8,83-87。
[81]孙健,康旺霖,魏修华,电子行业的企业创新能力评价指标体系研究,《当代财经》2007年02期。
[82]孙细明,张金隆,改进的AHP法在企业技术创新能力指标要素权重确定中的应用。科技进步与对策,2002(9)。
[83]唐炜,蒋日富,鹿盟,企业技术创新能力评价理论研究综述[J],科技进步与对策,2007,5(5)。
[84]王春迎,郝士琦等,基于结构自适应神经网络特征选择的一种改进方法,电光与控制,5(2005),32-35.
[85]王国胤,于洪,杨大春,基于条件信息熵的决策表约简,计算机学报,7(2002),759-766.
[86]王海威,朱建忠,许庆瑞,技术创新能力及其测度指标研究综述。中国地质大学学报(社会科学版),2005(5)。
[87]王建华,技术创新工程。北京:经济科学出版社,1996:32-46。
[88]王娟茹,潘杰义,技术创新能力评价探讨。科技进步与对策,2002(2)。
[89]王立新,高长春,任荣明,企业创新能力的评价体系和评价方法研究[J]。《东华大学学报:自然科学版》,2006年32卷3期34-37。
[90]王灵,俞金寿,二进制量子粒子群优化算法及其在化工过程故障诊断中的应用,华东理工大学学报(自然科学版),33(2007),692-696.
[91]王青云,饶扬德,企业技术创新绩效的层次灰色综合评判模型[J]。数量经济技术经济研究,2004,5:55-62。
[92]王彦鑫,中国家电产业竞争格局现状分析,山西财经大学学报,2002(5)。
[93]魏后凯,我国地区工业技术创新力评价[J]。中国工业经济,2004年05期。
[94]魏江,江浙沪地区大中型企业技术创新能力现状研究[J]。中国软科学,2002(2):98-102。
[95]魏江,郭斌,许庆瑞,企业技术能力与技术创新能力的评价指标体系。中国高科技企业评价,1999(5):29-34。
[96]魏江,许庆瑞,企业技术创新能力的概念、结构与评价。科学学与科学技术管理,1995(9):25-27。
[97]魏江,许庆瑞,企业技术能力与技术创新能力的协调性研究。科学管理研究,1996(4):28-32。
[98]魏江,许庆瑞,企业创新能力的概念、结构、度量与评价[J]。科学管理研究,1995:51-55。
[99]魏金宇:论制度创新系统的建立[J],《西北师范大学学报》(社会科学版),2000年第1期。
[100]魏梅,中国制造企业技术创新及其路径研究。西北农林科技大学博士论文,2007年。
[101]吴凤平,葛敏,耿晓娜,大型企业技术创新能力评价方法及应用。《科技与经济》,2005年05期。
[102]吴建斌,李太全,田茂,改进的遗传算法在白细胞识别中的应用研究,计算机工程与应用,43(27),2007,243-245.
[103]吴晓云,袁磊,中国家电行业的发展态势及营销战略选择。管理世界,2003(10):31-32。
[104]武妍,杨洋,基于判别式分析和神经网络的特征选择方法,计算机应用,2(2006),433-435.
[105]希普尔,技术创新的源泉[M],北京:商务印书馆,1992。
[106]夏维力,吕晓强,基于BP神经网络的企业技术创新能力评价及应用研究[J]。研究与发展管理,2005,17(1):50-54,72。
[107]徐博英,技术创新能力及其评价方法的发展变化[J]。科技和产业,2006年11期。
[108]许志晋,凌奕杰,宋凤珍。企业技术创新能力的模糊综合评判[J]。科学学研究,1997,15(1):105-110。
[109]杨宏进,企业技术创新能力评价指标的实证分析[J]。统计研究,1998:53-58。
[110]杨立才,李金亮,姚玉翠,吴晓晴,基于F-score特征选择和支持向量机的P300识别算法,生物医学工程学杂志,25(2008),23-26.
[111]约瑟夫·熊彼特[美]:经济发展理论[M],北京:商务印书馆1990年版。
[112]余光胜:《企业竞争优势根源的理论演进》,《外国经济与管理》,2002年10期。
[113]远德玉,企业技术创新能力的综合评价和动态分析方法,科学管理研究,1994(4):50-52。
[114]远德玉等:《企业技术创新概说》东北大学出版壮,1999年版.第32页。
[115]张国良,陈宏民,国内外技术创新能力指数化评价比较分析[J]。系统工程理论方法应用。2006,10(5)、
[116]张莉,孙钢,郭军,基于K-均值聚类的无监督的特征选择方法,计算机应用研究,3(2005),23-24.
[117]张丽新,王家,赵雁南,杨泽红.基于Relief的组合式特征选择,复旦学报(自然科学版),5(2004),893-898.
[118]张凌,基于DEA的企业技术创新项目评价与决策方法研究。哈尔滨工程大学博士论文,2006年。
[119]张瑞敏,创新是海尔持续发展的不竭动力。企业管理,2001,(10):45-47。
[120]张瑞敏,创新是海尔文化的灵魂。中国民营科技与经济,1999,(1):10-13。
[121]张炜,杨选良,自主创新概念的讨论与界定[J]。科学学研究,2006,(12):956-961。
[122]章新华,一种特征选择的动态规划方法,自动化学报,5(1998),675-680.
[123]赵彦云,中关村科技园区国际竞争力研究。管理世界,2001(4)。
[124]赵云,刘惟一,基于遗传算法的特征选择方法,计算机工程与应用,15(2004),52-54.
[125]郑春东、和金生,陈通,企业技术创新能力评价研究。企业决策,1999(10):108-110。
[126]邹林全,企业创新能力评价的比较,《统计与决策》,2008年08期。
[127]A,Smola A J,and Muller K R.Kernel principal component analysis.
[128]Aizerman M A,Braverman EM,Rozonoer L I.Theoretical foundations of the potential function method in pattern recognition learning.Journal of Machine Learning Research,2000,113-141,http://www.jmlr.org.
[129]Alain Rakotomamonjy.Variable Selection Using SVM-based Criteria,Journal of Machine Learning Research,3(2003),P1357-1370.
[130] Alvarez S , Barney J B. Entrepreneurial capabilities : A resource-based view. In G D Meyer and K A Heppard . Entrepreneurship as strategy. Thousand Oaks , CA: Sage Publications. 2000 .63-81
[ 131 ] Amidon,D.M,Knowledge Innovation,www.entovation.con/backgrnd, 14 May 1998.
[132] Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 1999.
[133] Anis Ben Ishak, Badih Ghattas. An Efficient Method for Variable Selection Using SVM-Based Criteria. Journal of Machine Learning Research, 1(2005), P1-31.
[134] Aronszajn N. Theory of reproducing kernels. Transactions of the American Mathematical Society, 1950, 68: 337-404.
[135] B, Burges C J C, Smola A J. Advances in Kernel Methods Support Vector Learning . MIT Press, 1999.
[ 136] B, Burges C J C, Smola A J. Advances in kernel methods-support vector learning. MIT Press, Cambridge, MA, 1999
[137] B, Burges C, Vapnik V. Incorporating invariances in support vector learning machines, Artificial Neural Networks ICANN'96, Springers Lecture Notes in Computer Science, Berlin, 1996,11(12): 47-52
[138] B, Burges C J C, and Smola A J, editors , Advances in Kernel Methods -Support Vector Learning, MIT Press, 1999, 327-352.
[139] B. Comparing support vector machines with Gaussian kernels to radial basis function classifier. IEEE Transactions on signal processing, 45(11), November, 1997
[ 140] B, Smola A, Muller K R. Kernel principal component analysis, In:Proc. of ICANN'97, 1997:583-589
[141] B, Smola A. A tutorial on support vector regression. Technical Report Series NC2-TR-1998-030, October, 1998
[ 142] B. Support Vector learning.R.Oldenbourg Verlag, 1997.
[143] Boser B E, Guyon I M, Vapnik V. A training algorithm for optimal margin classifiers .In Haussler D, editor, Proceedings of the 5 th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, ACM Press, July 1992,144-152.
[144] Belussi, F.&Arcangeli, F. A typology of networks: Flexible and evolutionary firms [J]. Research Policy, 1998, 27: 415-428.
[145] Ben-Tal,A. and Nemirovski,A. Rubust convex optimization, Math. Oper Res. 1998,(23):769-805.
[146] Ben-Tal,A. and Nemirovski,A. Rubust solutions to uncertain programs, Oper. Res. Letters, 1999,(25): 1-13.
[ 147] Bennett K P, Demiriz Z. Semi-supervised support vector machines. In:Proc. of NIPS'98, 1998
[ 148] Biemans, W.Gz Managing innovation within networks [M]. London: Routledge, 1992.
[ 149] Bogner, WC.&Thomas, H. Core competencies and competitive advantage: a model and illustrative evidence from the pharmaceutical industry [A]. In: Hamel, G, Heene, A. (Eds.),Competencies-based Competition[C]. New York: Wiley, 1994: 111 - 144.
[150] Breschi, S.&Malerba, F. Sectoral innovation systems: technological regimes, Schumpeterian dynamics and spatial boundaries [A]. In: Edquist, C. (ed.). Systems of Innovation[C]. London: Pinter, 1997.
[151] Brockhoff, K.&Chakrabarti, A .K. R&D/marketing linkage and innovation strategy: some West German experiences. IEEE Transaction on Engineering Management, 1988, 35:167-174.
[152] Brown, S.&Fai, F. Strategic resonance between technological and organisational capabilities in the innovation process within firms [J]. Technovation, 2006, 26: 60-75.
[153] Brown, S. et al. Strategic Operations Management [J]. Oxford: Butterworth Heinemann, 2000
[154] Brown, S.L.&Eisenhardt, K.M. Product development: past research, present findings, and future directions [J]. Academy of Management Review, 1995, 20(2): 343 -378.
[155] Burges C J C, Scholkopf B. Improving the accuracy and speed of support vector machines.In advances in neural information processing systems,Mozer M,Jordan M,and Petsche T,eds.Cambridge,MA,MIT Press,1997:375-381
[156]Burges C J C.Geometry and invariances in kernel based methods,in advances in kernel methods-support vector learning,Scholkopf B,Burges C.and Smola A,Eds.,Cambridge,MA,MIT Press,1999:89-116
[157].Caloghirou Y,Kastelli I,Tsakanikas A.Internal ca—pabilities and external knowledge sources:comple—ments or substitutes for innovative performance[J].Technovation,2004,24:29-39.
[158]Carlsson,B.&Eliasson,G.The nature and importance of economic competence[M].Stockholm,Sweden:Industrial Institute for Economic and Social Research(IUI),1991.
[159]Christiansen James A.Building the Innovative Organition._London:_MacMillan Press,2000
[160]Chew Hong-Gunn,Crisp D.J.,Bogner R.E.et al.Target detection in radar imagery using support vector machines with training size biasing[A].In:Proceedings of the sixth international conference on control,Automation,Robotics and Vision[C],Singapore,2000.
[161]Clark,K.B.&Fujimoto,T.Product development performance--strategy,organization,and management in the world auto industry 脚].Harvard:Harvard Business School Press,1991.
[162]Conant,J.S.,Mokwa,M.P&Varadarajan,PR.Strategic types,distinctive marketing competencies and organizational performance:a multiple measures based study[J].Strategic Management Journal,1990,11:365-383.
[163]Coombs,R.Core competencies&the strategic management of R&D[J].R&D Management,1996,26(4):345-355.
[164]Cooper,R.G&Kleinschmidt,E.J.Benchmarking the firm's critical success factors in new product development[J].Journal of Product Innovation Management,1995,12:374-91.
[165]Cooper,R.G The dimensions of industrial new product success and failure[J].Journal of Marketing,1997,43:93-103(Summer).
[166]Cortes C,Vapnik V.Support Vector Networks.Machine Learning,1995,(20):1-25
[167]Cover T M,The Best Two independent Measurements are not the Two Best,IEEE Trans,System Man Cybernetic,2(1974),P116-117.
[168]Czepiel,J.A.Patterns of interorganizational communication and diffusion of a major technological innovation in a competitive industrial community[J].Academy of Management Journal,1975,18(1):6-24.
[169]Davies,A.&Brady,T.Organizational capabilities and leaming in complex product systems:Towards repeatable solutions[J].Research Policy,2000,29:931-953.
[170]Day,US.The capabilities of market-driven organizations[J].Journal of Marketing,1994,58:37-52(October).
[171]De la Mothe,J.&Paquet,G Local and Regional Systems of Innovation[M].Norwell,MA:Kluwer Academic Publishers,1998.
[172]DebraM.Amidon Rogers.The Chalenge of Fifth Generation R&D[J].Research,Technology Management,1996:33-41。
[173]DeBresson,C.&Amesse,F.Networks of innovators:a review and introduction to the issue [J].Research Policy,1991,20(5):363-80.
[174]D L Barton.Core Capability & Core Rigidities:A Paradox in Managing New Product Development.Strategic Mgt,1992(13):56-61.
[175]Dodgson,M.,Gann,D.M.,&Salter,A.J.The intensification of innovation[J].International Journal of Innovation Management,2002,6(1):53-83.
[176]Dosi,G&Teece,D.J.Organizational competencies and boundaries of the firm[M].Berkeley,CA:University of California at Berkeley,1993.
[177]Edquist,C.Systems of Innovation[M].London:Pinter,1997.
[178]Eisenhardt,K.E.&Martin,J.A.Dynamic capabilities:what are they[J].Strategic Management Journal,2000,21:1105-1121.
[179]E1-Ghaoui,L.and Lebret,H.Rubust solutions to least-square problems to uncertain data matrices,SIAM J.Matrix Anal.Appl.1997,(18):1035-1064.
[180]EI-Ghaoui,L.,Oustry,F.,and Lebret,H.Rubust solutions to semidefinite programs,SIAM J.Optim.,1998,(9):33-52.
[181]ESTER,M.,KRIEGEL,H-P.,SANDER,J.and XU,X.1996.A density-based algorithm for discovering clusters in large spatial databases with noise.In Proceedings of the 2nd ACM SIGKDD,226-231,Portland,Oregon.
[182]Fleming,L.&Sorenson,O.Navigating the technology landscape of innovation[J],MIT Sloan Management Review,2003,44(2):15-23.
[183]Floricel,S.&Miller,R.2003.An exploratory comparison of the management of innovation in the new and old economies[J].R&D Management,33(5):501-525.
[184]Ford,D.Develop your technology strategy[J].Long Range Planning,1988,21(5):85-95.
[185]Foss,N.J.&Knundsen,C.Toward a Competence Theory of the Firm[M].Routledge,1996.
[186]Fowler,S.W,Wilcox King,A.,Marsh,S.J.&Victor,B.Beyond products:new strategic imperatives for developing competencies in dynamic environments.Journal of Engineering and Technology Management,2000,17(3-4):357-377.
[187]Freeman,C.Networks of innovators:A synthesis of Research issues[J].Research Policy,1991,20:499-514.
[188]Friess T.-T,Christianimi CN,Campbell C.The kernel adatron algorithm:a fast and simple learning procedure for support vector machines.In Proceeding of 15th Intl.Con Machine Learning.Morgan Kaufman Publishers,1998.
[189]G.Fung,O.L.Mangasarian,and J.Shavlik.Knowledge-based support vector machine classifiers.In Suzanna Becker,Sebastian Thrun,and Klaus Obermayer,editors,Advances in Neural Information Processing Systems 15,pages 521-528.MIT Press,Cambridge,MA,October 2003b.ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-09.ps.
[190]G.Fung,O.L.Mangasarian,and J.Shavlik.Knowledge-based nonlinear kernel classifiers.Technical Report 03-02,Data Mining Institute,Computer Sciences Department,University of Wisconsin,Madison,Wisconsin,March 2003.ftp://ftp.cs.wisc.edu/pub/dmi/techreports/03-02.ps.Conference on Learning Theory(COLT 03) and Workshop on Kernel Machines,Washington D.C.,August 24-27,2003.Proceedingsedited by M.Warmuth and B.Sch¨olkopf,Springer Verlag,Berlin,2003,102-113.
[191]Gallon,M.R.,Stillman,H.M.&Coates,D.Putting core competency thinking into practice [J].Research-Technology Management,1995,38(3):20-28.
[192]Gemunden,H.G&Heydebreck,P.The influence of business strategies on technological network activities[J].Research Policy,1995,24:831-849.
[193]Gemunden,H.G.Hogl,M.,Lechler,T.&Saad,A.Starting conditions of successful European R&D-consortia[A].In:Brockhof,K.,Chacrabarti,A.&Hauschildt,J.(eds.).The dynamics of innovation:strategical and managerial implications[C].Berlin:Springer,1999:237-75.
[194]Gemunden,H.G,Ritter,T.&Heydebreck,P.Network configuration and innovation success:an empirical analysis in German high-tech industries[J].International Journal of Research Marketing,1996,13(5):449-462.
[195]Gish H,Schimdt M.Text-indepenten speaker identification[J].EEE Trans on Signal Processing Magazine 1994.42(1),18-32.
[196]Gruner,K.E.&Homburg,C.Does customer interaction enhance new product success[J]?Journal of Business Research,2000,49(1):1-14.
[197]Guyon I,An Introduction to Variable and feature Selectiom M Machine Learning research,3(2003),P1157-1182.
[198]GUHA,S.,RASTOGI,R.,and SHIM,K.1999.ROCK:A robust clustering algorithm for categorical attributes.InProceedings of the 15th ICDE,512-521,Sydney,Australia.
[199]GUHA,S.,RASTOGI,R.,and SHIM,K.1998.CURE:An efficient clustering algorithm for large databases.In Proceedings of the ACM SIGMOD Conference,73-84,Seattle,WA.
[200]Hakansson,H.Corporate technological development:cooperation and networks[M].London:Routledge,1989.
[201]Hakansson,H.Industrial technological development:a network approach[M].London:Croom Helm,1987.
[202]Hall,R.A framework for identifying the intangible sources of sustainable competitive advantages[A].In:Hamel,G.,Heence,A.(Eds.),Competence-based Competition[C].New York:Wiley,1994:140-169.
[203]Hambrick,D.C.Environmental scanning and organizational strategy[J].Strategic Management Journal,1982,3(2):159-174.
[204]Hambrick,D.C.Some tests of the effectiveness and functional attributes of Miles and Snow's strategic types[J].Academy of Management Journal,1983,26:5:26(Mareh).
[205]Hamel,Gz&Heene,A.Competence-based Competition[M].Baffins Lane,Chichester:John Wiley&Sons,1994.
[206]Hamel,U&Prahalad,C.K.Competing for the Future[M].Cambridge:Harvard Business School Press,1994.
[207]Handfield,R.B.,Ragatz,CiL.,Petersen,K.J.&Monczka,R.M.Involving suppliers in new product development[J].California Management Review,1999,42:59-82(Fall).
[208]Haussller D.Convolution kernels on discrete structures.Technical Report UCSC-CRL-99-10,of California in Santa Cruz.Computer Science Department,July 1999.
[209]Heene,A.,&Sanchez,R.Competence-Based Strategic Management[M].Chichester:John Wiley,1997:127-150.
[210]Heisele B,et al.Hierarchical classification and feature reduction for fast face detection with support vectro machines[J].Pattern Recognition 2003,36,2007-2017.
[211]Helfat C.E.&Peteraf M.A.The dynamic resource-based view:capability lifecycles[J].Strategic Management Journal,2003(10):997-1010.
[212]Henderson R M,Clark K B.Architectural innovation:the reconfigration of existing product technologies and the failure of established firms'.Administrative Science Quarterly,1990,35(1):9-30.
[213]Hobday,M.Innovation in east Asia:diversity and development 协].In:Dodgson M.&Rothwell R.(eds.).The Handbook of Industrial Innovation[C],1994:94-105.
[214]Igor Kononenko,Estimating Attributes:Analysis and Extensions of RELIEF,Proceeding of the European conference on Machine Learning,1994,P171-182.
[215]J.MagQueen.Some methods for classification and analysis of multivariate observations.Proc.5th Berkeley Syrup.Math,Statist,Prob.,1967,1:281-197.
[216]J.Schmookler,Invention and Economic Growth[M],Harvard University press,1966。
[217]Jason Weston,Mike Tipping.Use of the Zero-Norm with Linear Models and Kernel Methods.Journal of Machine Learning Research.3(2003),P1439-1461.
[218]Janez Brank,Marko Grobelnik,N.Milic-Frayling,and Dunja Mladenic.Feature selection using linear support vector machines.Technical report,2002.
[219]Jennifer G.Dy,Carla E.Brodley.Feature Selection for Unsupervised Learning,Journal of Machine Learning Research,5(2004),P845-889.
[220]Javidan,M.Core competence:what does it mean in practice[J]? Long Range Planning,1998,31(1):60-71.
[221]Jennifer G.Dy,Carla E.Brodley.Feature Selection for Unsupervised Learning,Journal of Machine Learning Research,5(2004),P845-889.
[222]Ji Zhu,Saharon Rosset,Trevor Hastie,and Rob Tibshirani.1-norm support vector machines.In NIPS,2003.
[223]Jiawei Han and Micheline Kamber范明 孟小峰译,数据挖掘:概念与技术,北京:机械工业出版社,2001.
[224]Joachims T.Text categorization with support vector machines.Learning with many relevant features[C].In Proceedings of the European Conference on Machine Learning Berlin:Springe 1998:137-142.
[225]Johne,A.&Snelson,P.Successful New Product Development[M].Oxford:Blackwell,1998
[226]Julia Neumann,Christoph Schnorr,Gabriele Steidl.Combined SVM-based Feature Selection and Classification,Machine Learning.61(2005),P129-150.
[227]Kamien,M.&Schwartz,N.Market structure and innovation[J].Journal of Economic Literature,1975,23(1):1-37.
[228]KARYPIS,G.,HAN,E.-H.,and KUMAR,V.1999a.CHAMELEON:A hierarchical clustering algorithm using dynamic modeling,COMPUTER,32,68-75.
[229]Kaufrnan,L.and Rousseeuw,P.J.Clustering by means of Medoids,Statistical Data Analysis Based on the L1-Norm and Related Methods,North-Holland,1987,405-416.
[230]Kaufman,L.and Rousseeuw,P.J.Finding groups in data:an introduction to cluster analysis.New York:John Wiley and Sons,1990.
[231]Kay,J.Foundations of Corporate Success:How Business Strategies add Value[M].Oxford:Oxford University Press,1993:416.
[232]Kesler,M.,Klostad,D.&Clark,W.E.Third generation R&D:the key to leveraging core competence[J].The Columbia Journal of World Business,1993:34-44(Fall).
[233]Klein.J.,Gee,D.&Jones,H.Analysing clusters of skills in R&D core competencies,metaphors,visualization,&the role of IT[J].R&D Management,1998,28(1):37-42.
[234]Kline,S.&Rosenberg,N.An overview of innovation[A].In:the Positive Sum Strategy,Landau,R.&Rosenberg,N.(eds.)[C].Washington:National Academy Press,1986.
[235]Kira K,Rendell L.A practical approach to feature selection.Proceedings of the Ninth International Conference on Maching Learning,P249-256.
[236]L.Mangasarian.Knowledge-based linear programming.SIAM Journal on Optimization,15:375-382,2005.
[237]L.Xu and D.Schuurmans,Unsupervised and semi-supervised multi-class support vector machines,{AAAI-05,The Twentieth National Conference on Artificial Intelligence},2005.
[238]L.Xu,J.Neufeld,B.Larson and D.Schuurmans,Maximum margin clustering,{Advances in Neural Information Processing Systems 17(NIP S-04)},2004.
[239]LaBahn,D.W&Krapfel,R.Early supplier involvement in customer new product development:a contingency model of component supplier intentions[J].Journal of Business Research,2000,47(3):173-190.
[240]Langerak,F.,Peelen,E.&Nijssen,E.A laddering approach to the use of methods and techniques to reduce the cycle time of new-to-the-firm products[J].Journal of Product Innovation Management,1999,16:173-182.
[241]Lennard-barton,D.The organization as learning laboratory.Sloan Management Review,1992,34(1):23-38.
[242]Leonard-Barton,D.Core capability&core rigidities:a paradox in managing new product development[J],Strategic Management Journal,1992,13:111-125。
[243]Linli Xu,Dale Schuurmans.Unsupervised and Semi-supervised Multi-class Support Vector Machines.American Association for Artificial Intelligence(www.aaai.org).2005.
[244]Lior Wolf,Amnon Shashua.Feature Selection for Unsupervised and Supervised Inference:The Emergence of Sparsity in a Weight-Based Approach.Journal of Machine Learning Research,6(2005),P1855-1887.
[245]MacKay D.Introduction to Gaussian processes.In Neural Networks and Machine Learning(NATO Asi Series);Ed.Chris Bishop,1999.
[246]Mangasarian O.L,Musicant D.R.Lagrangian support vector machines.Journal of Machine Learning Research,2001,1:161-177.
[247]Mangasarian O.L,Musicant D.R.Successive overrelaxation for support vector machines.IEEE Trans.Neural Networks,1999,10(5):1032-1037.
[248]Mercer J.Functions of positive and negative type and their connection with the theory of integral equations.Philosophical Transactions of the Royal Society,London,1909,A209:415-446.
[249]Micchelli C A.Interpolation of scattered data:distance matrices and conditionally positive definite functions.Constructive approximation,1986,2:11-22.
[250]Michael hammer&James ChamPy.Reengineering the Corporation——a Manifesto for Business Revolution.HarPer Collins Publishers.Inc.New York,1993。
[251]M Kamien,Schwartz N,Market Structure and Innovation[M]。 Cambridge University Press,Cambridge,19820
[252]N.Cristianini,J.Shawe-Taylor,and C.Campbell.Dynamically adapting kernels in support vector machines.In M.S.Keams,S.A.Solla,and D.A.Cohn,editors,\textit{Advances in Neural Information Processing Systems,11.}MIT Press,1998.
[253]Ng R.T.,and Han J.1994.Efficient and effective clustering methods for spatial data mining,Proc.20th Int.Conf.on Very Large Data Bases,144-155.Santiago,Chile.Protein Data Bank,1994.
[254]Osuna E,Freund R,Girosi F.Improved training algorithm for support vector machines[C].In 7th IEEE workshop on Neural Networks for signal Processing NNSP'97 IEEE 1997,276-285.
[255]Prahalad,C.K &Hamel,G,The core competence of the corporation[J],Harvard Business Review,1990,68(3):79-91。
[256]P.S.Bradleyy,O.L.Mangasariany,W.N.Streetz.Feature Selection via Mathematical Programming,INFORMS Journal on Computing,2(1998),P209-217.
[257]Robert A Burgelmanetal,Strategic Management of Technology and Innovation.Second Edition,Mcgraw-Hill,1996:117-158.
[258]Scholkopf B,Smola A,Williamson R.C,et al.New suppod vector algorithms[J].Neural Computation,2000,12(5):1207-1245.
[259]Siedlecki W,Sklansky J,A Note Genetic algorithm for Large-scale Feature Selection,Pattern Recognition Letters,11(1989):P335-347.
[260]Sim,Melvyn,Robust Optimization,Phd.Thesis,June 2004.
[261]Smola A.Generalization bounds for convex combinations of kernel functions.Alex J.Smola,GMD.NeuroCOLT2 Technical Report series,NC2-TR-1998-020,July,1998
[262]Smola A.Learning with kernels.PH.D thesis,1998
[263]Souitaris V.Technological trajectories as moderators of firm—level determinants of innovation[J].Re—search Policy,2002,31:877-898.
[264]Stephen Boyd and Lieven Vandenberghe,Convex Optimization,Cambridge University Press,2004.
[265]Suykens J,Vandewalle J.Least square support vector machine classifiers.Neural Processing Leters,1999,9(3):293-300.
[266]Suykens J,Branbanter J.D,Lukas L,et al.Weighted least squares support vector machines:robustness and spare approximation[J].Neurocomputing,2002,48(1):85-105.
[267]Vapnik V,Chervoknenkis A.On the uniform convergence of relative frequencies of evens to their probabilities.Doklady Akademii Nauk USSR,181(4),1968
[268]Vapnik V,Chervoknenkis A.On the uniform convergence of relative frequencies of evens to their probabilities.Theory of Probability and its Application,1971,16(2):264-280
[269]Vapnik V.Estimation of dependence based on empirical data.New York,Springer-Verlag,1982
[270]Vapnik V,Lemer A.Pattern recognition using generalized portrait method.Automation and Remote Control,(24),1963
[271]Vapnik V The Nature of statistical learning theory.Springer,NY,1995.张学工译,统计学习理论的本质,清华大学出版社,2000
[272]Vapnik V.Statistical learning theory[M].New York:Wiley,1998
[273]Vorhies D W,Im S,Morgan N A,Product innovation capabilities:acquiring and using knowledge to develop innovative products,American Marketing Association,Conference Proceedings,2002。
[274]Wahba G.Spline Models for observational Data.Volume 59 of CBMS-NSF Regional Conference Series in Applied Mathematices.SIAM,1990.
[275]Wahba G.Support vector machines,reproducing kernel Hilbert spaces and the randomized GACV.In Sch\"{o}lkopf B,Burges C J C,and Smola A J,editors,Advances in Kernel Methods -Support Vector Learning,MIT Press,1999,69-88.
[276]Watkins C.Dynamic alignment kernels.Technical Report CSD-TR-98-11,Royal Holloway,University of London,Computer Science Department,January 1999.
[277]Watkins C.Dynamic alignment kernels.In Smola A J,Bartlett P,Sch\"{o}lkopf B,and Schuurmans C,editors,Advances in Large Margin classifiers.MIT Press,1999.
[278]Watkins C.Kernels from matching operations.Technical Report CSD-TR-98-07,Royal Holloway,University of London,Computer Science Department,July 1999.
[279]Weston J.Extensions to the support vector method.Ph.D thesis,Royal Holloway University of London,1999
[280]Weston J,Mukherjee S,Chapelle O,Pontil M,Poggio T,Vapnik.Feature Selection for SVMs.Advances in neural information processing systems,Cambridge,MA:MIT Press.13(2001),P668-674.
[281] Yves Grandvalet, Stephane Canu. Adaptive Scaling for Feature Selection in SVMs, In Advances in Neural Information Processing System 15, MIT Press, 2003.
[282] Zhili Wu, Chunhung Li. Feature Selection for Classification using Transductive Support Vector Machines.2004.10
[283] ZHANG, T, RAMAKRISHNAN, R, AND LIVNY, M.1996. BIRCH: An efficient data clustering method for very large databases. SIGMOD Rec. 25, 2, 103-114.
[284] Zhang X G Using class-center vectors to build support vector machines, NNSP'99, 1999
[2]2007年中国冰箱行业研究报告,中华商务网,2008年,5月。
[3]2007年电冰箱/冷柜/冰箱压缩机研究发展报告,中国家用电器协会,2008年,3月。
[4]2007年全球冰箱(冰柜)生产布局及市场分布研究报告,中华商务网,2008年,4月。
[5]2007年度中国中央空调行业发展报告,艾肯空调制冷网,2008年,2月。
[6]2007——2008冷年全球家用空调行业年度研究报告,中华商务网,2007年,10月。
[7]2007——2008冷年中国家用空调行业年度研究报告,中华商务网,2007年,9月。
[8]2007——2008冷年中国家用空调行业年度研究报告
[9]2007电冰箱/冷柜/冰箱压缩机研究发展报告
[10]2008年中国白色家电产业发展研究年度报告,赛迪顾问股份有限公司,2008年,5月。
[11]2008年中国家用电器发展研究报告,中国家用电器协会,2008年,3月。
[12]2008年第一季度中国洗衣机市场研究报告,互联网消费调研中心,2008年,4月。
[13]2008年度中国空调行业白皮书,苏宁电器,2008年,5月。
[14]艾米顿[美]:知识经济的创新战略—智慧的觉醒[M],周金英译,北京:新华出版社1998年版,第87-88页。
[15]白俊红,江可申,李婧,林雷芳,企业技术创新能力测度与评价的因子分析模型及其应用,中国软科学,2008年3期。
[16]察志敏等,我国工业企业技术创新能力评价方法及实证研究[J]。统计研究,2004,(3):12-16。
[17]陈彬,洪家荣等,最优特征子集选择问题,计算机学报,2(1997),133-138.
[18]陈劲,童亮等,复杂产品系统创新评估指标体系研究[J]。研究与发展管理,2003:60-65。
[19]陈晓慧,企业技术创新能力的模糊综合[J],科技进步与对策,2002,19,(5):127-129。
[20]曹崇延,王淮学,企业技术创新能力评价指标体系研究。预测,1998(2):66-68。
[21]董岗,傅铅生,关于企业创新能力的评价模型研究:《商业研究》,2004年9期,33-36页。
[22]邓乃扬,田英杰.数据挖掘中的最优化方法—支持向量机.北京:科学出版社.2004.
[23]杜栋,企业技术创新评价的DEA方法[J]。系统工程理论方法应用,2001,10(1):82-84。
[24]范柏乃,城市技术创新透视[M],北京:机械工业出版社,2003 12。
[25]杜海涛,家电市场未饱和的依据何在[N],城乡机电信息报,2002.12.4。
[26]傅家骥,技术创新学。北京:清华大学出版社,1998:37-42,314-333。
[27]傅家骥,技术创新——中国企业发展之路。北京:企业管理出版社,1992:81-164。
[28]傅家骥:《技术经济学》,北京:清华大学出版社,1998 P2-3。
[29]郭璇,杨晓元,刘佳,韩鹏,基于遗传算法和一类SVM的隐秘图像检测方案,计算机工程与应用,43(20),2007,37-39+55.
[30]何传启,张风:知识创新——竞争新焦点[M],北京:经济管理出版社2001年版。
[31]何玉梅,龚灏,黄晴,四川省工业企业自主创新能力评测体系研究。《科技进步与对策》,2008年25卷5期。
[32]贾沛,特征选择技术研究,华中科技大学,2003年.
[33]姜炳鳞,谢延宇,企业技术创新能力评价指标体系及其多级模糊评价方法[J]。商业 研究,2004,18(502):77-79。
[34]江兵,企业技术创新系统运行机制与评价研究。合肥工业大学博士论文,2007年。
[35]蒋琴儿,曹艳春.绿色贸易壁垒对我国家电行业的影响及对策.企业活力,2004(5):15-17。
[36]韩景元,庞更新等,企业技术创新能力评价的理论、指标和方法[J],河北科技大学学报,2002,23,(3):90-94。
[37]韩立达,四川长虹打造产业航母的实证分析及战略构想。软科学,2003(4):17。
[38]何传启,张风:知识创新——竞争新焦点[M],北京:经济管理出版社2001年版。
[39]胡恩华,企业技术创新能力指标体系的构建及综合评价,科研管理。2001(4):79-84。
[40]胡小锋,赵辉.图像处理与识别应用案例精选(Visual C++/Matlab),人民邮电出版社,2004.9.
[41]胡志坚:国家创新系统——理论分析与国际比较[M],北京:社会科学文献出版社2000年版,第7页。
[42]科兹洛夫斯基:《结构主义及其反辩证法的性质》,载《国外社会科学》,1979(2)。
[43]李国训,中国家电企业跨国经营战略研究。武汉大学,2003。
[44]李金明,企业创新能力的分析模型,《东北大学学报》,2001年第4期。
[45]李金明,基于知识基础的企业创新能力研究。上海交通大学博士论文,2001年。
[46]李良成,中国家电品牌产品进入发达国家市场的四种商业模式。企业经济,2004(10):11-13。
[47]李敏强,寇纪淞,戴林,示例学习与特征选择的规划模型方法,系统工程学报,2(2006),163.167.
[48]李庆东,技术创新能力评价指标体系与评价方法研究。《现代情报》,2005年9期。
[49]李媛,企业技术创新的多层次分析。东北大学博士论文,2006年。
[50]李云,特征选择算法及其在基于内容图像检索中的应用研究,重庆大学博士学位论文,2005年.
[51]梁静国,姜金贵,企业创新能力的BP神经网络评价研究[J]。《物流科技》2004,11,79-81。
[52]廖春良,冯宗宪,程发新,中国家电产业国际价格竞争力分析。价格理论与实践,2004,3。
[53]凌锦江,陈兆乾,周志华,基于特征选择的神经网络集成方法,复旦大学学报,5(2004),685-688.
[54]刘凤朝等,基于集对分析法的区域自主创新能力评价研究[J]。中国软科学,2005(11):83-106.
[55]刘海月,何燕,论中国家电企业核心能力之培育[[J]。企业经济,2005,4:106-108。
[56]刘红燕,曹艺,21世纪中国家电业国际化发展战略[J]。经济师,2004,5:44-45。
[57]刘素华,候惠芳,李小霞,基于遗传算法和模拟退火算法的特征选择方法,计算机工程,16(2005),157-159.
[58]刘文军,李洪兴等,一种求粗糙集中最小属性约简的新方法,北京师范大学学报,1(2004),8-12.
[59]刘耀,黄新建,张滨松,许智宏,创新型企业创新能力评价指标体系研究。《南昌大学学报(人文社会科学版)》,2008年01期。
[60]刘勇国,李学明,基于遗传算法的特征子集选择,计算机工程,6(2003),19-20.
[61]柳卸林,中国创新管理前沿(第一辑),北京:北京理工大学出版社,2004年。
[62]路风:《动态企业理论的发展》,《国际经济评论》,2000年9-10,P46-510。
[63]卢怀宝等,企业技术创新能力的二次相对评价法[J],大庆石油学院学报,2002,26,(1):90-93。
[64]栾大龙 铉一民 姚彬 赵焕丛,基于粗糙集——主成分分析的企业创新能力评价实证研究,《计算机工程与应用》,2007年43卷4期,207-209页。
[65]马胜杰,企业技术创新能力及其评价指标体系,数量经济技术经济研究。2002(12):5-8。
[66]马贤娣,庄宇,安会刚,基于偏好型DEA的企业技术创新能力评价,《工业工程》,2007年10卷6期。
[67]马歇尔,经济原理,商务印书馆,北京,1997,P257。
[68]马歇尔,A:《经济学原理》(1920),朱志泰,译,商务印书馆,1981。
[69]尼古莱.J.福斯、克里斯蒂安.克努森:《企业万能:面向企业能力理论》,大连:东北财经大学出版社,1998年版,P28-29。
[70]乔立岩,彭喜元,马云彤,基于遗传算法和支持向量机的特征子集选择方法,20(2006),1-4.
[71]任荣伟,赵盈盈,“微笑曲线效应”下的公司海外并购式内创业战略整合——以TCL 的海外并购式内创业活动的胜败为例。现代管理科学,2007年10期。
[72]邵兴东,中国家电企业国际化战略研究—海尔、海信、TCL的国际化比较。青岛,中国海洋大学,2003。
[73]斯蒂芬,M,夏彼洛:《永续创新》(中文),北京:电子工业出版社,2003年1月,P19。
[74]石奇,论企业创新能力的构成与促进,《南京经济学院学报》,2002年第2期。
[75]史晓燕,企业技术创新能力指标体系设置及综合评价。陕西经贸学院学报,1999(4):27-30。
[76]宋河发,穆荣平等,自主创新及创新自主性测度研究[J]。中国软科学,2006,(6):60-66。
[77]宋志红,企业创新能力来源的是正研究。对外经济贸易大学博士论文,2006年。
[78]舒辉,论企业创新能力的基本结构及培育途径,《工业技术经济》,2003年第2期。
[79]苏越良,罗剑宏,企业技术创新能力的灰色关联分析,《中南工业大学学报:社会科学版》,2002年8卷2期,120-122。
[80]孙冰,李柏洲,企业技术创新动力的评价指标体系。《改革》,2005.8,83-87。
[81]孙健,康旺霖,魏修华,电子行业的企业创新能力评价指标体系研究,《当代财经》2007年02期。
[82]孙细明,张金隆,改进的AHP法在企业技术创新能力指标要素权重确定中的应用。科技进步与对策,2002(9)。
[83]唐炜,蒋日富,鹿盟,企业技术创新能力评价理论研究综述[J],科技进步与对策,2007,5(5)。
[84]王春迎,郝士琦等,基于结构自适应神经网络特征选择的一种改进方法,电光与控制,5(2005),32-35.
[85]王国胤,于洪,杨大春,基于条件信息熵的决策表约简,计算机学报,7(2002),759-766.
[86]王海威,朱建忠,许庆瑞,技术创新能力及其测度指标研究综述。中国地质大学学报(社会科学版),2005(5)。
[87]王建华,技术创新工程。北京:经济科学出版社,1996:32-46。
[88]王娟茹,潘杰义,技术创新能力评价探讨。科技进步与对策,2002(2)。
[89]王立新,高长春,任荣明,企业创新能力的评价体系和评价方法研究[J]。《东华大学学报:自然科学版》,2006年32卷3期34-37。
[90]王灵,俞金寿,二进制量子粒子群优化算法及其在化工过程故障诊断中的应用,华东理工大学学报(自然科学版),33(2007),692-696.
[91]王青云,饶扬德,企业技术创新绩效的层次灰色综合评判模型[J]。数量经济技术经济研究,2004,5:55-62。
[92]王彦鑫,中国家电产业竞争格局现状分析,山西财经大学学报,2002(5)。
[93]魏后凯,我国地区工业技术创新力评价[J]。中国工业经济,2004年05期。
[94]魏江,江浙沪地区大中型企业技术创新能力现状研究[J]。中国软科学,2002(2):98-102。
[95]魏江,郭斌,许庆瑞,企业技术能力与技术创新能力的评价指标体系。中国高科技企业评价,1999(5):29-34。
[96]魏江,许庆瑞,企业技术创新能力的概念、结构与评价。科学学与科学技术管理,1995(9):25-27。
[97]魏江,许庆瑞,企业技术能力与技术创新能力的协调性研究。科学管理研究,1996(4):28-32。
[98]魏江,许庆瑞,企业创新能力的概念、结构、度量与评价[J]。科学管理研究,1995:51-55。
[99]魏金宇:论制度创新系统的建立[J],《西北师范大学学报》(社会科学版),2000年第1期。
[100]魏梅,中国制造企业技术创新及其路径研究。西北农林科技大学博士论文,2007年。
[101]吴凤平,葛敏,耿晓娜,大型企业技术创新能力评价方法及应用。《科技与经济》,2005年05期。
[102]吴建斌,李太全,田茂,改进的遗传算法在白细胞识别中的应用研究,计算机工程与应用,43(27),2007,243-245.
[103]吴晓云,袁磊,中国家电行业的发展态势及营销战略选择。管理世界,2003(10):31-32。
[104]武妍,杨洋,基于判别式分析和神经网络的特征选择方法,计算机应用,2(2006),433-435.
[105]希普尔,技术创新的源泉[M],北京:商务印书馆,1992。
[106]夏维力,吕晓强,基于BP神经网络的企业技术创新能力评价及应用研究[J]。研究与发展管理,2005,17(1):50-54,72。
[107]徐博英,技术创新能力及其评价方法的发展变化[J]。科技和产业,2006年11期。
[108]许志晋,凌奕杰,宋凤珍。企业技术创新能力的模糊综合评判[J]。科学学研究,1997,15(1):105-110。
[109]杨宏进,企业技术创新能力评价指标的实证分析[J]。统计研究,1998:53-58。
[110]杨立才,李金亮,姚玉翠,吴晓晴,基于F-score特征选择和支持向量机的P300识别算法,生物医学工程学杂志,25(2008),23-26.
[111]约瑟夫·熊彼特[美]:经济发展理论[M],北京:商务印书馆1990年版。
[112]余光胜:《企业竞争优势根源的理论演进》,《外国经济与管理》,2002年10期。
[113]远德玉,企业技术创新能力的综合评价和动态分析方法,科学管理研究,1994(4):50-52。
[114]远德玉等:《企业技术创新概说》东北大学出版壮,1999年版.第32页。
[115]张国良,陈宏民,国内外技术创新能力指数化评价比较分析[J]。系统工程理论方法应用。2006,10(5)、
[116]张莉,孙钢,郭军,基于K-均值聚类的无监督的特征选择方法,计算机应用研究,3(2005),23-24.
[117]张丽新,王家,赵雁南,杨泽红.基于Relief的组合式特征选择,复旦学报(自然科学版),5(2004),893-898.
[118]张凌,基于DEA的企业技术创新项目评价与决策方法研究。哈尔滨工程大学博士论文,2006年。
[119]张瑞敏,创新是海尔持续发展的不竭动力。企业管理,2001,(10):45-47。
[120]张瑞敏,创新是海尔文化的灵魂。中国民营科技与经济,1999,(1):10-13。
[121]张炜,杨选良,自主创新概念的讨论与界定[J]。科学学研究,2006,(12):956-961。
[122]章新华,一种特征选择的动态规划方法,自动化学报,5(1998),675-680.
[123]赵彦云,中关村科技园区国际竞争力研究。管理世界,2001(4)。
[124]赵云,刘惟一,基于遗传算法的特征选择方法,计算机工程与应用,15(2004),52-54.
[125]郑春东、和金生,陈通,企业技术创新能力评价研究。企业决策,1999(10):108-110。
[126]邹林全,企业创新能力评价的比较,《统计与决策》,2008年08期。
[127]A,Smola A J,and Muller K R.Kernel principal component analysis.
[128]Aizerman M A,Braverman EM,Rozonoer L I.Theoretical foundations of the potential function method in pattern recognition learning.Journal of Machine Learning Research,2000,113-141,http://www.jmlr.org.
[129]Alain Rakotomamonjy.Variable Selection Using SVM-based Criteria,Journal of Machine Learning Research,3(2003),P1357-1370.
[130] Alvarez S , Barney J B. Entrepreneurial capabilities : A resource-based view. In G D Meyer and K A Heppard . Entrepreneurship as strategy. Thousand Oaks , CA: Sage Publications. 2000 .63-81
[ 131 ] Amidon,D.M,Knowledge Innovation,www.entovation.con/backgrnd, 14 May 1998.
[132] Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 1999.
[133] Anis Ben Ishak, Badih Ghattas. An Efficient Method for Variable Selection Using SVM-Based Criteria. Journal of Machine Learning Research, 1(2005), P1-31.
[134] Aronszajn N. Theory of reproducing kernels. Transactions of the American Mathematical Society, 1950, 68: 337-404.
[135] B, Burges C J C, Smola A J. Advances in Kernel Methods Support Vector Learning . MIT Press, 1999.
[ 136] B, Burges C J C, Smola A J. Advances in kernel methods-support vector learning. MIT Press, Cambridge, MA, 1999
[137] B, Burges C, Vapnik V. Incorporating invariances in support vector learning machines, Artificial Neural Networks ICANN'96, Springers Lecture Notes in Computer Science, Berlin, 1996,11(12): 47-52
[138] B, Burges C J C, and Smola A J, editors , Advances in Kernel Methods -Support Vector Learning, MIT Press, 1999, 327-352.
[139] B. Comparing support vector machines with Gaussian kernels to radial basis function classifier. IEEE Transactions on signal processing, 45(11), November, 1997
[ 140] B, Smola A, Muller K R. Kernel principal component analysis, In:Proc. of ICANN'97, 1997:583-589
[141] B, Smola A. A tutorial on support vector regression. Technical Report Series NC2-TR-1998-030, October, 1998
[ 142] B. Support Vector learning.R.Oldenbourg Verlag, 1997.
[143] Boser B E, Guyon I M, Vapnik V. A training algorithm for optimal margin classifiers .In Haussler D, editor, Proceedings of the 5 th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, ACM Press, July 1992,144-152.
[144] Belussi, F.&Arcangeli, F. A typology of networks: Flexible and evolutionary firms [J]. Research Policy, 1998, 27: 415-428.
[145] Ben-Tal,A. and Nemirovski,A. Rubust convex optimization, Math. Oper Res. 1998,(23):769-805.
[146] Ben-Tal,A. and Nemirovski,A. Rubust solutions to uncertain programs, Oper. Res. Letters, 1999,(25): 1-13.
[ 147] Bennett K P, Demiriz Z. Semi-supervised support vector machines. In:Proc. of NIPS'98, 1998
[ 148] Biemans, W.Gz Managing innovation within networks [M]. London: Routledge, 1992.
[ 149] Bogner, WC.&Thomas, H. Core competencies and competitive advantage: a model and illustrative evidence from the pharmaceutical industry [A]. In: Hamel, G, Heene, A. (Eds.),Competencies-based Competition[C]. New York: Wiley, 1994: 111 - 144.
[150] Breschi, S.&Malerba, F. Sectoral innovation systems: technological regimes, Schumpeterian dynamics and spatial boundaries [A]. In: Edquist, C. (ed.). Systems of Innovation[C]. London: Pinter, 1997.
[151] Brockhoff, K.&Chakrabarti, A .K. R&D/marketing linkage and innovation strategy: some West German experiences. IEEE Transaction on Engineering Management, 1988, 35:167-174.
[152] Brown, S.&Fai, F. Strategic resonance between technological and organisational capabilities in the innovation process within firms [J]. Technovation, 2006, 26: 60-75.
[153] Brown, S. et al. Strategic Operations Management [J]. Oxford: Butterworth Heinemann, 2000
[154] Brown, S.L.&Eisenhardt, K.M. Product development: past research, present findings, and future directions [J]. Academy of Management Review, 1995, 20(2): 343 -378.
[155] Burges C J C, Scholkopf B. Improving the accuracy and speed of support vector machines.In advances in neural information processing systems,Mozer M,Jordan M,and Petsche T,eds.Cambridge,MA,MIT Press,1997:375-381
[156]Burges C J C.Geometry and invariances in kernel based methods,in advances in kernel methods-support vector learning,Scholkopf B,Burges C.and Smola A,Eds.,Cambridge,MA,MIT Press,1999:89-116
[157].Caloghirou Y,Kastelli I,Tsakanikas A.Internal ca—pabilities and external knowledge sources:comple—ments or substitutes for innovative performance[J].Technovation,2004,24:29-39.
[158]Carlsson,B.&Eliasson,G.The nature and importance of economic competence[M].Stockholm,Sweden:Industrial Institute for Economic and Social Research(IUI),1991.
[159]Christiansen James A.Building the Innovative Organition._London:_MacMillan Press,2000
[160]Chew Hong-Gunn,Crisp D.J.,Bogner R.E.et al.Target detection in radar imagery using support vector machines with training size biasing[A].In:Proceedings of the sixth international conference on control,Automation,Robotics and Vision[C],Singapore,2000.
[161]Clark,K.B.&Fujimoto,T.Product development performance--strategy,organization,and management in the world auto industry 脚].Harvard:Harvard Business School Press,1991.
[162]Conant,J.S.,Mokwa,M.P&Varadarajan,PR.Strategic types,distinctive marketing competencies and organizational performance:a multiple measures based study[J].Strategic Management Journal,1990,11:365-383.
[163]Coombs,R.Core competencies&the strategic management of R&D[J].R&D Management,1996,26(4):345-355.
[164]Cooper,R.G&Kleinschmidt,E.J.Benchmarking the firm's critical success factors in new product development[J].Journal of Product Innovation Management,1995,12:374-91.
[165]Cooper,R.G The dimensions of industrial new product success and failure[J].Journal of Marketing,1997,43:93-103(Summer).
[166]Cortes C,Vapnik V.Support Vector Networks.Machine Learning,1995,(20):1-25
[167]Cover T M,The Best Two independent Measurements are not the Two Best,IEEE Trans,System Man Cybernetic,2(1974),P116-117.
[168]Czepiel,J.A.Patterns of interorganizational communication and diffusion of a major technological innovation in a competitive industrial community[J].Academy of Management Journal,1975,18(1):6-24.
[169]Davies,A.&Brady,T.Organizational capabilities and leaming in complex product systems:Towards repeatable solutions[J].Research Policy,2000,29:931-953.
[170]Day,US.The capabilities of market-driven organizations[J].Journal of Marketing,1994,58:37-52(October).
[171]De la Mothe,J.&Paquet,G Local and Regional Systems of Innovation[M].Norwell,MA:Kluwer Academic Publishers,1998.
[172]DebraM.Amidon Rogers.The Chalenge of Fifth Generation R&D[J].Research,Technology Management,1996:33-41。
[173]DeBresson,C.&Amesse,F.Networks of innovators:a review and introduction to the issue [J].Research Policy,1991,20(5):363-80.
[174]D L Barton.Core Capability & Core Rigidities:A Paradox in Managing New Product Development.Strategic Mgt,1992(13):56-61.
[175]Dodgson,M.,Gann,D.M.,&Salter,A.J.The intensification of innovation[J].International Journal of Innovation Management,2002,6(1):53-83.
[176]Dosi,G&Teece,D.J.Organizational competencies and boundaries of the firm[M].Berkeley,CA:University of California at Berkeley,1993.
[177]Edquist,C.Systems of Innovation[M].London:Pinter,1997.
[178]Eisenhardt,K.E.&Martin,J.A.Dynamic capabilities:what are they[J].Strategic Management Journal,2000,21:1105-1121.
[179]E1-Ghaoui,L.and Lebret,H.Rubust solutions to least-square problems to uncertain data matrices,SIAM J.Matrix Anal.Appl.1997,(18):1035-1064.
[180]EI-Ghaoui,L.,Oustry,F.,and Lebret,H.Rubust solutions to semidefinite programs,SIAM J.Optim.,1998,(9):33-52.
[181]ESTER,M.,KRIEGEL,H-P.,SANDER,J.and XU,X.1996.A density-based algorithm for discovering clusters in large spatial databases with noise.In Proceedings of the 2nd ACM SIGKDD,226-231,Portland,Oregon.
[182]Fleming,L.&Sorenson,O.Navigating the technology landscape of innovation[J],MIT Sloan Management Review,2003,44(2):15-23.
[183]Floricel,S.&Miller,R.2003.An exploratory comparison of the management of innovation in the new and old economies[J].R&D Management,33(5):501-525.
[184]Ford,D.Develop your technology strategy[J].Long Range Planning,1988,21(5):85-95.
[185]Foss,N.J.&Knundsen,C.Toward a Competence Theory of the Firm[M].Routledge,1996.
[186]Fowler,S.W,Wilcox King,A.,Marsh,S.J.&Victor,B.Beyond products:new strategic imperatives for developing competencies in dynamic environments.Journal of Engineering and Technology Management,2000,17(3-4):357-377.
[187]Freeman,C.Networks of innovators:A synthesis of Research issues[J].Research Policy,1991,20:499-514.
[188]Friess T.-T,Christianimi CN,Campbell C.The kernel adatron algorithm:a fast and simple learning procedure for support vector machines.In Proceeding of 15th Intl.Con Machine Learning.Morgan Kaufman Publishers,1998.
[189]G.Fung,O.L.Mangasarian,and J.Shavlik.Knowledge-based support vector machine classifiers.In Suzanna Becker,Sebastian Thrun,and Klaus Obermayer,editors,Advances in Neural Information Processing Systems 15,pages 521-528.MIT Press,Cambridge,MA,October 2003b.ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-09.ps.
[190]G.Fung,O.L.Mangasarian,and J.Shavlik.Knowledge-based nonlinear kernel classifiers.Technical Report 03-02,Data Mining Institute,Computer Sciences Department,University of Wisconsin,Madison,Wisconsin,March 2003.ftp://ftp.cs.wisc.edu/pub/dmi/techreports/03-02.ps.Conference on Learning Theory(COLT 03) and Workshop on Kernel Machines,Washington D.C.,August 24-27,2003.Proceedingsedited by M.Warmuth and B.Sch¨olkopf,Springer Verlag,Berlin,2003,102-113.
[191]Gallon,M.R.,Stillman,H.M.&Coates,D.Putting core competency thinking into practice [J].Research-Technology Management,1995,38(3):20-28.
[192]Gemunden,H.G&Heydebreck,P.The influence of business strategies on technological network activities[J].Research Policy,1995,24:831-849.
[193]Gemunden,H.G.Hogl,M.,Lechler,T.&Saad,A.Starting conditions of successful European R&D-consortia[A].In:Brockhof,K.,Chacrabarti,A.&Hauschildt,J.(eds.).The dynamics of innovation:strategical and managerial implications[C].Berlin:Springer,1999:237-75.
[194]Gemunden,H.G,Ritter,T.&Heydebreck,P.Network configuration and innovation success:an empirical analysis in German high-tech industries[J].International Journal of Research Marketing,1996,13(5):449-462.
[195]Gish H,Schimdt M.Text-indepenten speaker identification[J].EEE Trans on Signal Processing Magazine 1994.42(1),18-32.
[196]Gruner,K.E.&Homburg,C.Does customer interaction enhance new product success[J]?Journal of Business Research,2000,49(1):1-14.
[197]Guyon I,An Introduction to Variable and feature Selectiom M Machine Learning research,3(2003),P1157-1182.
[198]GUHA,S.,RASTOGI,R.,and SHIM,K.1999.ROCK:A robust clustering algorithm for categorical attributes.InProceedings of the 15th ICDE,512-521,Sydney,Australia.
[199]GUHA,S.,RASTOGI,R.,and SHIM,K.1998.CURE:An efficient clustering algorithm for large databases.In Proceedings of the ACM SIGMOD Conference,73-84,Seattle,WA.
[200]Hakansson,H.Corporate technological development:cooperation and networks[M].London:Routledge,1989.
[201]Hakansson,H.Industrial technological development:a network approach[M].London:Croom Helm,1987.
[202]Hall,R.A framework for identifying the intangible sources of sustainable competitive advantages[A].In:Hamel,G.,Heence,A.(Eds.),Competence-based Competition[C].New York:Wiley,1994:140-169.
[203]Hambrick,D.C.Environmental scanning and organizational strategy[J].Strategic Management Journal,1982,3(2):159-174.
[204]Hambrick,D.C.Some tests of the effectiveness and functional attributes of Miles and Snow's strategic types[J].Academy of Management Journal,1983,26:5:26(Mareh).
[205]Hamel,Gz&Heene,A.Competence-based Competition[M].Baffins Lane,Chichester:John Wiley&Sons,1994.
[206]Hamel,U&Prahalad,C.K.Competing for the Future[M].Cambridge:Harvard Business School Press,1994.
[207]Handfield,R.B.,Ragatz,CiL.,Petersen,K.J.&Monczka,R.M.Involving suppliers in new product development[J].California Management Review,1999,42:59-82(Fall).
[208]Haussller D.Convolution kernels on discrete structures.Technical Report UCSC-CRL-99-10,of California in Santa Cruz.Computer Science Department,July 1999.
[209]Heene,A.,&Sanchez,R.Competence-Based Strategic Management[M].Chichester:John Wiley,1997:127-150.
[210]Heisele B,et al.Hierarchical classification and feature reduction for fast face detection with support vectro machines[J].Pattern Recognition 2003,36,2007-2017.
[211]Helfat C.E.&Peteraf M.A.The dynamic resource-based view:capability lifecycles[J].Strategic Management Journal,2003(10):997-1010.
[212]Henderson R M,Clark K B.Architectural innovation:the reconfigration of existing product technologies and the failure of established firms'.Administrative Science Quarterly,1990,35(1):9-30.
[213]Hobday,M.Innovation in east Asia:diversity and development 协].In:Dodgson M.&Rothwell R.(eds.).The Handbook of Industrial Innovation[C],1994:94-105.
[214]Igor Kononenko,Estimating Attributes:Analysis and Extensions of RELIEF,Proceeding of the European conference on Machine Learning,1994,P171-182.
[215]J.MagQueen.Some methods for classification and analysis of multivariate observations.Proc.5th Berkeley Syrup.Math,Statist,Prob.,1967,1:281-197.
[216]J.Schmookler,Invention and Economic Growth[M],Harvard University press,1966。
[217]Jason Weston,Mike Tipping.Use of the Zero-Norm with Linear Models and Kernel Methods.Journal of Machine Learning Research.3(2003),P1439-1461.
[218]Janez Brank,Marko Grobelnik,N.Milic-Frayling,and Dunja Mladenic.Feature selection using linear support vector machines.Technical report,2002.
[219]Jennifer G.Dy,Carla E.Brodley.Feature Selection for Unsupervised Learning,Journal of Machine Learning Research,5(2004),P845-889.
[220]Javidan,M.Core competence:what does it mean in practice[J]? Long Range Planning,1998,31(1):60-71.
[221]Jennifer G.Dy,Carla E.Brodley.Feature Selection for Unsupervised Learning,Journal of Machine Learning Research,5(2004),P845-889.
[222]Ji Zhu,Saharon Rosset,Trevor Hastie,and Rob Tibshirani.1-norm support vector machines.In NIPS,2003.
[223]Jiawei Han and Micheline Kamber范明 孟小峰译,数据挖掘:概念与技术,北京:机械工业出版社,2001.
[224]Joachims T.Text categorization with support vector machines.Learning with many relevant features[C].In Proceedings of the European Conference on Machine Learning Berlin:Springe 1998:137-142.
[225]Johne,A.&Snelson,P.Successful New Product Development[M].Oxford:Blackwell,1998
[226]Julia Neumann,Christoph Schnorr,Gabriele Steidl.Combined SVM-based Feature Selection and Classification,Machine Learning.61(2005),P129-150.
[227]Kamien,M.&Schwartz,N.Market structure and innovation[J].Journal of Economic Literature,1975,23(1):1-37.
[228]KARYPIS,G.,HAN,E.-H.,and KUMAR,V.1999a.CHAMELEON:A hierarchical clustering algorithm using dynamic modeling,COMPUTER,32,68-75.
[229]Kaufrnan,L.and Rousseeuw,P.J.Clustering by means of Medoids,Statistical Data Analysis Based on the L1-Norm and Related Methods,North-Holland,1987,405-416.
[230]Kaufman,L.and Rousseeuw,P.J.Finding groups in data:an introduction to cluster analysis.New York:John Wiley and Sons,1990.
[231]Kay,J.Foundations of Corporate Success:How Business Strategies add Value[M].Oxford:Oxford University Press,1993:416.
[232]Kesler,M.,Klostad,D.&Clark,W.E.Third generation R&D:the key to leveraging core competence[J].The Columbia Journal of World Business,1993:34-44(Fall).
[233]Klein.J.,Gee,D.&Jones,H.Analysing clusters of skills in R&D core competencies,metaphors,visualization,&the role of IT[J].R&D Management,1998,28(1):37-42.
[234]Kline,S.&Rosenberg,N.An overview of innovation[A].In:the Positive Sum Strategy,Landau,R.&Rosenberg,N.(eds.)[C].Washington:National Academy Press,1986.
[235]Kira K,Rendell L.A practical approach to feature selection.Proceedings of the Ninth International Conference on Maching Learning,P249-256.
[236]L.Mangasarian.Knowledge-based linear programming.SIAM Journal on Optimization,15:375-382,2005.
[237]L.Xu and D.Schuurmans,Unsupervised and semi-supervised multi-class support vector machines,{AAAI-05,The Twentieth National Conference on Artificial Intelligence},2005.
[238]L.Xu,J.Neufeld,B.Larson and D.Schuurmans,Maximum margin clustering,{Advances in Neural Information Processing Systems 17(NIP S-04)},2004.
[239]LaBahn,D.W&Krapfel,R.Early supplier involvement in customer new product development:a contingency model of component supplier intentions[J].Journal of Business Research,2000,47(3):173-190.
[240]Langerak,F.,Peelen,E.&Nijssen,E.A laddering approach to the use of methods and techniques to reduce the cycle time of new-to-the-firm products[J].Journal of Product Innovation Management,1999,16:173-182.
[241]Lennard-barton,D.The organization as learning laboratory.Sloan Management Review,1992,34(1):23-38.
[242]Leonard-Barton,D.Core capability&core rigidities:a paradox in managing new product development[J],Strategic Management Journal,1992,13:111-125。
[243]Linli Xu,Dale Schuurmans.Unsupervised and Semi-supervised Multi-class Support Vector Machines.American Association for Artificial Intelligence(www.aaai.org).2005.
[244]Lior Wolf,Amnon Shashua.Feature Selection for Unsupervised and Supervised Inference:The Emergence of Sparsity in a Weight-Based Approach.Journal of Machine Learning Research,6(2005),P1855-1887.
[245]MacKay D.Introduction to Gaussian processes.In Neural Networks and Machine Learning(NATO Asi Series);Ed.Chris Bishop,1999.
[246]Mangasarian O.L,Musicant D.R.Lagrangian support vector machines.Journal of Machine Learning Research,2001,1:161-177.
[247]Mangasarian O.L,Musicant D.R.Successive overrelaxation for support vector machines.IEEE Trans.Neural Networks,1999,10(5):1032-1037.
[248]Mercer J.Functions of positive and negative type and their connection with the theory of integral equations.Philosophical Transactions of the Royal Society,London,1909,A209:415-446.
[249]Micchelli C A.Interpolation of scattered data:distance matrices and conditionally positive definite functions.Constructive approximation,1986,2:11-22.
[250]Michael hammer&James ChamPy.Reengineering the Corporation——a Manifesto for Business Revolution.HarPer Collins Publishers.Inc.New York,1993。
[251]M Kamien,Schwartz N,Market Structure and Innovation[M]。 Cambridge University Press,Cambridge,19820
[252]N.Cristianini,J.Shawe-Taylor,and C.Campbell.Dynamically adapting kernels in support vector machines.In M.S.Keams,S.A.Solla,and D.A.Cohn,editors,\textit{Advances in Neural Information Processing Systems,11.}MIT Press,1998.
[253]Ng R.T.,and Han J.1994.Efficient and effective clustering methods for spatial data mining,Proc.20th Int.Conf.on Very Large Data Bases,144-155.Santiago,Chile.Protein Data Bank,1994.
[254]Osuna E,Freund R,Girosi F.Improved training algorithm for support vector machines[C].In 7th IEEE workshop on Neural Networks for signal Processing NNSP'97 IEEE 1997,276-285.
[255]Prahalad,C.K &Hamel,G,The core competence of the corporation[J],Harvard Business Review,1990,68(3):79-91。
[256]P.S.Bradleyy,O.L.Mangasariany,W.N.Streetz.Feature Selection via Mathematical Programming,INFORMS Journal on Computing,2(1998),P209-217.
[257]Robert A Burgelmanetal,Strategic Management of Technology and Innovation.Second Edition,Mcgraw-Hill,1996:117-158.
[258]Scholkopf B,Smola A,Williamson R.C,et al.New suppod vector algorithms[J].Neural Computation,2000,12(5):1207-1245.
[259]Siedlecki W,Sklansky J,A Note Genetic algorithm for Large-scale Feature Selection,Pattern Recognition Letters,11(1989):P335-347.
[260]Sim,Melvyn,Robust Optimization,Phd.Thesis,June 2004.
[261]Smola A.Generalization bounds for convex combinations of kernel functions.Alex J.Smola,GMD.NeuroCOLT2 Technical Report series,NC2-TR-1998-020,July,1998
[262]Smola A.Learning with kernels.PH.D thesis,1998
[263]Souitaris V.Technological trajectories as moderators of firm—level determinants of innovation[J].Re—search Policy,2002,31:877-898.
[264]Stephen Boyd and Lieven Vandenberghe,Convex Optimization,Cambridge University Press,2004.
[265]Suykens J,Vandewalle J.Least square support vector machine classifiers.Neural Processing Leters,1999,9(3):293-300.
[266]Suykens J,Branbanter J.D,Lukas L,et al.Weighted least squares support vector machines:robustness and spare approximation[J].Neurocomputing,2002,48(1):85-105.
[267]Vapnik V,Chervoknenkis A.On the uniform convergence of relative frequencies of evens to their probabilities.Doklady Akademii Nauk USSR,181(4),1968
[268]Vapnik V,Chervoknenkis A.On the uniform convergence of relative frequencies of evens to their probabilities.Theory of Probability and its Application,1971,16(2):264-280
[269]Vapnik V.Estimation of dependence based on empirical data.New York,Springer-Verlag,1982
[270]Vapnik V,Lemer A.Pattern recognition using generalized portrait method.Automation and Remote Control,(24),1963
[271]Vapnik V The Nature of statistical learning theory.Springer,NY,1995.张学工译,统计学习理论的本质,清华大学出版社,2000
[272]Vapnik V.Statistical learning theory[M].New York:Wiley,1998
[273]Vorhies D W,Im S,Morgan N A,Product innovation capabilities:acquiring and using knowledge to develop innovative products,American Marketing Association,Conference Proceedings,2002。
[274]Wahba G.Spline Models for observational Data.Volume 59 of CBMS-NSF Regional Conference Series in Applied Mathematices.SIAM,1990.
[275]Wahba G.Support vector machines,reproducing kernel Hilbert spaces and the randomized GACV.In Sch\"{o}lkopf B,Burges C J C,and Smola A J,editors,Advances in Kernel Methods -Support Vector Learning,MIT Press,1999,69-88.
[276]Watkins C.Dynamic alignment kernels.Technical Report CSD-TR-98-11,Royal Holloway,University of London,Computer Science Department,January 1999.
[277]Watkins C.Dynamic alignment kernels.In Smola A J,Bartlett P,Sch\"{o}lkopf B,and Schuurmans C,editors,Advances in Large Margin classifiers.MIT Press,1999.
[278]Watkins C.Kernels from matching operations.Technical Report CSD-TR-98-07,Royal Holloway,University of London,Computer Science Department,July 1999.
[279]Weston J.Extensions to the support vector method.Ph.D thesis,Royal Holloway University of London,1999
[280]Weston J,Mukherjee S,Chapelle O,Pontil M,Poggio T,Vapnik.Feature Selection for SVMs.Advances in neural information processing systems,Cambridge,MA:MIT Press.13(2001),P668-674.
[281] Yves Grandvalet, Stephane Canu. Adaptive Scaling for Feature Selection in SVMs, In Advances in Neural Information Processing System 15, MIT Press, 2003.
[282] Zhili Wu, Chunhung Li. Feature Selection for Classification using Transductive Support Vector Machines.2004.10
[283] ZHANG, T, RAMAKRISHNAN, R, AND LIVNY, M.1996. BIRCH: An efficient data clustering method for very large databases. SIGMOD Rec. 25, 2, 103-114.
[284] Zhang X G Using class-center vectors to build support vector machines, NNSP'99, 1999
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |