Webb statistical pattern recognition 3rd 2011Wiley
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StatisticalPatternRecognition
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StatisticalPatternRecognition
Thirdedition
AndrewR.Webb.KeithDCopsey
MathematicsandDataAnalysisConsultancy,Malvern,UK
时WILEY
AJohnwileySons,Ltd,Publication
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LibraryofCongressCataloging-in-PublicationData
Webb,A.R(AndrewR)
Statisticalpatternrecognition/AndrewR.webb,KeithDCopsey.-3rded
Includesbibliographicalreferencesandindex
ISBN9780-470-68227-2(hardback)-ISBN9780-470-68228-9(paper)
LPatternperception-Statisticalmethods.I.Copsey,KeithD.Il.Title
Q327.W432011
006.4dc23
20110249
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HBISBN:978-0-470-68227-2
PBISBN:978-0-470-68228-9
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ePubisbn:978-1-11996140-6
Mobiisbn:978-1-11996141-3
Typesetin10/12ptTimesbyAptaraInc.NewDelhi,India
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ToRosemary
Samuel.miriam,Jacobandethan
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Contents
Preface
XIX
Notation
IntroductiontoStatisticalPatternRecognition
1.1StatisticalPatternrecognition
1.1.1Introductio
1.1.2TheBasicmodel
1.2StagesinaPatternRecognitionProblem
4
1.3Issues
6
4ApproachestoStatisticalPatternRecognition
7
1.5ElementaryDecisionTheory
1.5.1Bayes'DecisionRuleforMinimumError
1.5.2Bayes'DecisionRuleforMinimumError-RejectOption
12
1.5.3Bayes'DecisionRuleforMinimumRisk
13
1.5.4Bayes'DecisionRuleforMinimumRisk-RejectOption
15
1.5.5Neyman-PearsonDecisionrule
15
1.5.6MinimaxCriterion
18
1.5.7Discussion
1.6Discriminantfunctions
20
1.6.1Introducti
20
1.6.2Lineardiscriminantfunctions
21
1.6.3PiecewiseLineardiscriminantFunctions
23
1.6.4GeneralisedLineardiscriminantFunction
24
1.6.5Summary
26
1.7Multipleregression
27
1.8Outlineofbook
29
1.9NotesandReferences
29
Exercises
2DensityEstimation-Parametric
2.1Introduction
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CONTENTS
2.2EstimatingtheParametersofthedistributions
2.2.1EstimativeApproach
34
2.2.2PredictiveApproach
2.3TheGaussianClassifier
2.3.1Specification
2.3.2DerivationoftheGaussianClassifierPlug-InEstimates
37
2.3.3ExampleApplicationStudy
39
2.4DealingwithSingularitiesinthegaussianClassifier
2.4.1Introduction
40
2.4.2Naiveb:
40
2.4.3ProjectionontoaSubspace
41
2.4.4Lineardiscriminantfunction
41
2.4.5RegularisedDiscriminantAnalysis
42
2.4.6ExampleApplicationStudy
2.4.7FurtherDevelopments
45
2.4.8Summary
46
2.5FinitemixtureModels
46
2.5.1Introduction
46
2.5.2MixtureModelsfordiscrimination
48
2.5.3ParameterEstimationforNormalmixturemodels
49
2.5.4NormalMixtureModelCovariancematrixConstraints
2.5.5HowManyComponents
52
2.5.6Maximumlikelihoodestimationviaem
2.5.7ExampleApplicationStudy
60
2.5.8FurtherDevelopments
62
2.5.9Summary
63
2.6ApplicationStudies
63
2.7SummaryandDiscussion
66
2.8Recommendations
66
2.9Notesandreferences
67
Exercises
67
3Densityestimation-bayesian
3.1Introduction
3.1.1Basics
3.1.2Recursivecalculation
3.1.3Proportionality
73
3.2AnalyticSolutions
3.2.1ConjugatePriors
3.2.2EstimatingtheMeanofaNormalDistributionwith
Knownvariance
75
3.2.3EstimatingtheMeanandtheCovarianceMatrixofamultivariate
Normaldistribution
3.2.4UnknownPriorClassprobabilities
3.2.5S
ummary
87
3.3BayesianSamplingSchemes
87
3.3.1Introduction
87
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