统计模式识别研究进展
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摘要
研究了统计模式识别研究的主要新进展。介绍了统计模式识别的原理和方法。从类条件概率分布的估计、线性判别法、贝叶斯分类器、误差界以及新的模式识别模型等方面概述了近几年有关统计模式识别方面的研究进展。最后进行了评述。
Major recent developments of statistical pattern recognition are reviewed First, the principle and methods of statistical pattern recognition are described Then, the newly research of statistical pattern recognition is reviewed, which consists of class-conditional-density estimation,linear discriminant methods,Bayes classifier,error bounds and some novel pattern recognition models Finally, some comments about pattern recognition are commented
Major recent developments of statistical pattern recognition are reviewed First, the principle and methods of statistical pattern recognition are described Then, the newly research of statistical pattern recognition is reviewed, which consists of class-conditional-density estimation,linear discriminant methods,Bayes classifier,error bounds and some novel pattern recognition models Finally, some comments about pattern recognition are commented
引文
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5罗公亮.从神经网络到支撑矢量机(中).冶金自动化,2001;25(6):1～4.
6罗公亮.从神经网络到支撑矢量机(下).冶金自动化,2002;26(1):1～5.
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11罗公亮.核函数(上).冶金自动化,2002;26(3):1～4.
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21HerbrichR,GraepelT.APAC-Bayesianmarginboundforlinearclassifiers.InformationTheory,IEEETransactionson,2002;48(12):3140～3150.
22MuruaA.Upperboundsforerrorratesoflinearcombinationsofclas-sifiers.PatternAnalysisandMachineIntelligence,IEEETransac-tionson,2002;24(5):591～602.
23TitsiasMK,LikasAC.Sharedkernelmodelsforclassconditionaldensityestimation.NeuralNetworks,IEEETransactionson,2001;12(5):987～997.
24韩祯祥,张琦,文福拴.粗糙集理论及其应用综述.控制理论与应用,1999;16(2):153～157.
25胡可云,陆玉昌,石纯一.粗糙集理论及其应用进展.清华大学学报(自然科学版),2001;41(1):64～68.
26BorkowskiM,PetersJF.Approximatingsensorsignals:aroughsetapproach.ElectricalandComputerEngineering,2002.IEEECCECE2002.CanadianConferenceon,2002;Vol.2:980～985.
27WangJunsheng,LiMinqiang.Amulti-factorialdecision-makingmodelfordeductionofrulesinroughsets.IntelligentControlandAutomation,2000.Proceedingsofthe3rdWorldCongresson,2000;Vol.1:383～386.
28GuXP,TsoSK,ZhangQ.Combinationofroughsettheoryandartificialneuralnetworksfortransientstabilityassessment.PowerSystemTechnology,2000.Proceedings.PowerCon2000.Interna-tionalConferenceon,2000;Vol.1:19～24.
29王守觉.仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用.电子学报,2002;30(10):1417～1420.
30PardoM,SberveglieriG.Learningfromdata:atutorialwithempha-sisonmodernpatternrecognitionmethods.SensorsJournal,IEEE,2002;2(3):203～217.
2王碧泉,陈祖荫.模式识别理论、方法和应用.北京:地震出版社,1989.
3赵陵滋,甘云祥.统计模式识别算法的MATLAB语言实现.应用科技,2002;29(6):12～13.
4罗公亮.从神经网络到支撑矢量机(上).冶金自动化,2001;0(5):1～5.
5罗公亮.从神经网络到支撑矢量机(中).冶金自动化,2001;25(6):1～4.
6罗公亮.从神经网络到支撑矢量机(下).冶金自动化,2002;26(1):1～5.
7JainAK,DuinRPW,JianchangMao.Statisticalpatternrecogni-tion:areview.PatternAnalysisandMachineIntelligence,IEEETransactionson,2000;22(1):4～37.
8UjiieH,OmachiS,AsoH.Adiscriminantfunctionconsideringnor-malityimprovementofthedistribution.PatternRecognition,2002.Proceedings.16thInternationalConferenceon,2002;Vol.2:224～227.
9IwamuraM,OmachiS,AsoH.Amodificationofeigenvaluestocompensateestimationerrorsofeigenvectors.PatternRecognition,2000.Proceedings.15thInternationalConferenceon,2000;Vol.2:378～381.
10张学工.关于统计学习理论与支持向量机.自动化学报,2000;26(1):32～44.
11罗公亮.核函数(上).冶金自动化,2002;26(3):1～4.
12GuttmanO,MeirR.NonlinearFisherdiscriminantusingkernels.ElectricalandElectronicEngineersinIsrael,2000.The21stIEEEConventionofthe,2000;vol.1:257～260.
13罗公亮.核函数(下).冶金自动化,2002;26(4):1～4.
14CookeT.TwovariationsonFisher'slineardiscriminantforpatternrecognition.PatternAnalysisandMachineIntelligence,IEEETrans-actionson,2002;24(2):268～273.
15RuedaLG,OommenBJ.Onoptimalpairwiselinearclassifiersfornormaldistributions:thetwo-dimensionalcase.PatternAnalysisandMachineIntelligence,IEEETransactionson,2002;24(2):274～280.
16LiuJNK,LiBNL,DillonTS.AnimprovednaiveBayesianclas-sifiertechniquecoupledwithanovelinputsolutionmethod[infallprediction].Systems,ManandCybernetics,PartC,IEEETransac-tionson,2001;31(2):249～256.
17MenhajMB,DelgoshaF.Asoftprobabilisticneuralnetworkforim-plementationofBayesianclassifiers.NeuralNetworks,2001.Pro-ceedings.IJCNN'01.InternationalJointConferenceon,2001;vol.1:454～458.
18FerlandG,YeapT.AchievingoptimalBayesianclassificationperfor-manceusinganovelapproach:the‘racetotheattractor’neuralnetworkmodel.VirtualandIntelligentMeasurementSystems,2001.IEEEInternationalWorkshopon.VIMS2001;Vol.1:115～120.
19YingZhu,SchwartzS.Discriminantanalysisandadaptivewaveletfeatureselectionforstatisticalobjectdetection.PatternRecognition,2002.Proceedings.16thInternationalConferenceon,2002;Vol.4:86～89.
20SchulterR,NeyH.Model-basedMCEboundtothetrueBayes'error.IEEESignalProcessingLetters,2001;8(5):131～133.
21HerbrichR,GraepelT.APAC-Bayesianmarginboundforlinearclassifiers.InformationTheory,IEEETransactionson,2002;48(12):3140～3150.
22MuruaA.Upperboundsforerrorratesoflinearcombinationsofclas-sifiers.PatternAnalysisandMachineIntelligence,IEEETransac-tionson,2002;24(5):591～602.
23TitsiasMK,LikasAC.Sharedkernelmodelsforclassconditionaldensityestimation.NeuralNetworks,IEEETransactionson,2001;12(5):987～997.
24韩祯祥,张琦,文福拴.粗糙集理论及其应用综述.控制理论与应用,1999;16(2):153～157.
25胡可云,陆玉昌,石纯一.粗糙集理论及其应用进展.清华大学学报(自然科学版),2001;41(1):64～68.
26BorkowskiM,PetersJF.Approximatingsensorsignals:aroughsetapproach.ElectricalandComputerEngineering,2002.IEEECCECE2002.CanadianConferenceon,2002;Vol.2:980～985.
27WangJunsheng,LiMinqiang.Amulti-factorialdecision-makingmodelfordeductionofrulesinroughsets.IntelligentControlandAutomation,2000.Proceedingsofthe3rdWorldCongresson,2000;Vol.1:383～386.
28GuXP,TsoSK,ZhangQ.Combinationofroughsettheoryandartificialneuralnetworksfortransientstabilityassessment.PowerSystemTechnology,2000.Proceedings.PowerCon2000.Interna-tionalConferenceon,2000;Vol.1:19～24.
29王守觉.仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用.电子学报,2002;30(10):1417～1420.
30PardoM,SberveglieriG.Learningfromdata:atutorialwithempha-sisonmodernpatternrecognitionmethods.SensorsJournal,IEEE,2002;2(3):203～217.
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