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1、GaussianProcessesforMachineLearningcomputerscience/machinelearningGaussianProcessesforMachineLearningCarlEdwardRasmussenOfrelatedinterestGaussianProcessesforMachineLearningCarlEdwardRasmussenandChristopherK.I.WilliamsIntroductiontoMachineLearningEthemAlpaydinGaussianprocesses(GPs)provideapri
2、ncipled,practical,probabilisticapproachtolearninginkernelmachines.Acomprehensivetextbookonthesubject,coveringabroadarrayoftopicsnotusuallyincludedinintroductoryGPshavereceivedincreasedattentioninthemachine-machinelearningtexts.Inordertopresentaunifiedtreatmentofmachinelearningproblemsandsolu
3、tions,itlearningcommunityoverthepastdecade,andthisbookdiscussesmanymethodsfromdifferentfields,includingstatistics,patternrecognition,neuralnetworks,artifi-providesalong-neededsystematicandunifiedtreat-cialintelligence,signalprocessing,control,anddatamining.mentoftheoreticalandpracticalaspect
4、sofGPsinmachinelearning.ThetreatmentiscomprehensiveandLearningKernelClassifiersself-contained,targetedatresearchersandstudentsinTheoryandAlgorithmsmachinelearningandappliedstatistics.RalfHerbrichChristopherK.I.WilliamsThebookdealswiththesupervised-learningprob-Thisbookprovidesacomprehensiveo
5、verviewofboththetheoryandalgorithmsofkernelclassifiers,includinglemforbothregressionandclassification,andincludesthemostrecentdevelopments.Itdescribesthemajoralgorithmicadvances—kernelperceptronlearning,kerneldetailedalgorithms.Awidevarietyofcovariance(kernel)CarlEdwardRasmussenisaResearchSc
6、ientistattheFisherdiscriminants,supportvectormachines,relevancevectormachines,Gaussianprocesses,andBayespointRasmussenandWilliamsfunctionsarepresentedandtheirpropertiesdiscussed.DepartmentofEmpiricalInferenceforMachinemachines—andprovidesadetailedintroductiontolearningtheory,includingVCandPA
7、C-Bayesiantheory,ModelselectionisdiscussedbothfromaBayesianandaLearningandPerceptionattheMaxPlanckInstitutedata-dependentstructuralriskminimization,andcompressionbounds.classicalperspective.Manyconnectionstootherwell-forBiologicalCybernetics,Tübing
