下载
加入VIP
  • 专属下载券
  • 上传内容扩展
  • 资料优先审核
  • 免费资料无限下载

上传资料

关闭

关闭

关闭

封号提示

内容

首页 The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory.pdf

The Nature of Statistical Learn…

brian
2010-01-30 0人阅读 举报 0 0 0 暂无简介

简介:本文档为《The Nature of Statistical Learning Theorypdf》,可适用于IT/计算机领域

CoverPrefacetotheFirstEditionAcknowledgementsContentsIntroduction:FourPeriodsintheResearchoftheLearningProblemSettingoftheLearningProblemFunctionEstimationModelTheProblemofRiskMinimizationThreeMainLearningProblemsTheGeneralSettingoftheLearningProblemTheEmpiricalRiskMinimization(ERM)InductionPrincipleTheFourPartsofLearningTheoryInformationReasoningandCommentsTheClassicalParadigmofSolvingLearningProblemsNonparametricMethodsofDensityEstimationMainPrincipleforSolvingProblemsUsingaRestrictedAmountofInformationModelMinimizationoftheRiskBasedonEmpiricalDataStochasticApproximationInferenceConsistencyofLearningProcessesTheClassicalDefinitionofConsistencyandtheConceptofNontrivialConsistencyTheKeyTheoremofLearningTheoryNecessaryandSufficientConditionsforUniformTwosidedConvergenceNecessaryandSufficientConditionsforUniformOnesidedConvergenceTheoryofNonfalsiflabilityTheoremsonNonfalsifiabilityThreeMilestonesinLearningTheoryInformalReasoningandCommentsTheBasicProblemsofProbabilityTheoryandStatisticsTwoModesofEstimatingaProbabilityMeasureStrongModeEstimationofProbabilityMeasuresandtheDensityEstimationProblemTheGlivenkoCantelliTheoremandItsGenerationMathematicalTheoryofInductionBoundsontheRateofConvergenceofLearningProcessesTheBasicInequalitiesGeneralizationfortheSetofRealFunctionsTheMainDistributionIndependentBoundsBoundsontheGeneralizationAbilityofLearningMachinesTheStructuresoftheGrowthFunctionTheVCDimensionofaSetofFunctionsConstructiveDistributionIndependentBoundsTheProblemsofConstructingRigorous(DistributionDependent)BoundsInformalReasoningandCommentsKolmogorovSmirnovDistributionsRacingfortheConstantBoundsonEmpiricalProcessesControllingtheGeneralizationAbilityofLearningProcessesStructuralRiskMinimization(SRM)InductivePrincipleAsymptoticAnalysisoftheRateofConvergenceTheProblemofFunctionApproximationinLearningTheoryExamplesofStructuresforNeuralNetsTheProblemofLocalFunctionEstimationTheMinimumDescriptionLength(MDL)andSRMPrinciplesInformalReasoningandCommentsMethodsforSolvingIllposedProblemsStochasticIllposedProblemsandtheProblemofDensityEstimationTheProblemofPolynomialApproximationoftheRegressionTheProblemofCapacityControlTheProblemofCapacityControlandBayesianInferenceMethodsofPatternRecognitionWhyCanLearningMachinesGeneralizeSigmoidApproximationofInductiveFunctionsNeuralNetworksTheOptimalSeparatingHyperplaneConstructingtheOptimalHyperplaneSupportVector(SV)MachinesExperimentswithSVMachinesRemarksonSVMachinesSVMandLogisticRegressionEnsembleoftheSVMInformalReasoningandCommentsTheArtofEngineeringVersusFormalInferenceWisdomofStatisticalModelsWhatCanOneLearnfromDigitRecognitionExperimentsMethodsofFunctionEstimationepsilonInsensitiveLossFunctionsSVMforEstimatingRegressionFunctionConstructingKernalsforEstimatingRealvaluedFunctionsKernelsGeneratingSplinesKernelsGeneratingFourierExpansionsTheSupportVectorANOVADecomposition(SVAD)forFunctionApproximationandRegressionEstimationSVMforSolvingLinearOperatorEquationsFunctionApproximationUsingtheSVMSVMforRegressionEstimationInformalReasoningandCommentsLossFunctionsfortheRegressionEstimationProblemLossFunctionsforRobustEstimatorsSupportVectorRegressionMachineDirectMethodsinStatisticalLearningTheoryProblemofEstimatingDensities,ConditionalProbabilities,andConditionalDensitiesTheProblemofSolvinganApproximatelyDeterminedIntegralEquationGlivenkoCantelliThoeremIllposedProblemsThreeMethodsofSolvingIllposedProblemsMainAssertionsoftheTheoryofIllposedProblemsNonparametricMethodsofDensityEstimationSVMSolutionoftheDensityEstimationProblemsConditionalProbabilityEstimationEstimationofConditionalDensityandRegressionRemarksInformalReasoningandCommentsThreeElementsofaScientificTheoryStochasticIllposedProblemsTheVicinalRiskMinimizationPrincipleandtheSVMsTheVicinalRiskMinimizationPrincipleVRMMethodsforthePatternRecognitionProblemsExamplesofVicinalKernelsNonsymmetricVicinitiesGeneralizationforEstimationRealvaluedFunctionsEstimatingDensityandConditionalDensityInformalReasoningandCommentsConclusion:WhatIsImportantinLearningTheoryWhatisImportantintheSettingoftheProblemWhatisImportantintheTheoryofConsistencyofLearningProcessesWhatisImportantintheTheoryofBoundsWhatisImportantintheTheoryforControllingtheGeneralizationAbilityofLearningMachinesWhatisImportantintheTheoryforConstructingLearningAlgorithmsWhatistheMostImportantReferencesRemarksonReferencesACDMNRSZ

用户评价(0)

关闭

新课改视野下建构高中语文教学实验成果报告(32KB)

抱歉,积分不足下载失败,请稍后再试!

提示

试读已结束,如需要继续阅读或者下载,敬请购买!

评分:

/33

VIP

意见
反馈

免费
邮箱

爱问共享资料服务号

扫描关注领取更多福利