Decision T - Statistics

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BayesianLearninginProbabilisticDecisionT reesMichaelI JordanMITCollaboratorsRobertJacobs 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 < < < dropthedatasetdownthetree a

Ba y esian Learning in Probabilistic Decision T rees Mic hael I Jordan MIT Col lab or ators Rob ert Jacobs Ro c hester Lei Xu Hong Kong Georey Hin ton T oron to Stev

  Decision, Decision t