Transcription of Decision T - Statistics
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Decision T - Statistics
BayesianLearninginProbabilisticDecisionT reesMichaelI JordanMITC ollaboratorsRobertJacobs Rochester LeiXu HongKong Geo reyHinton Toronto StevenNowlan Synaptics MarinaMeila MIT LawrenceSaul MIT Outline decisiontrees probabilisticdecisiontrees EMalgorithmandextensions modelselection Bayesiancomputations empiricalresults systemidenti cation classi cation theoreticalresults trainingseterror testseterrorSomeproblemswithmulti layeredneuralnetworks thelearningalgorithmsareslow hardtounderstandthenetwork hardtobuildinpriorknowledge poorperformanceonnon stationarydata notnaturalforsomefunctionsSupervisedlear ning akaregression classi cation Weassumethatthelearnerisprovidedwithatra iningset X f x t y t gT wherexisaninputvectorandyisanoutputvecto r Wewillgaugeperformanceonatestset Xs f x t y t gTs Decisiontreesx < <
Comp onen t Mo dels De cision mo dels P i j x is a classication mo del an y parametric classicati on mo del is appropriate w euse am ultinomial logit mo del
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