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 < < < dropthedatasetdownthetree
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
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