russ greiner cxkejgrcsualbertaca one important question learning parameters goal produce generative model many standard algorithms training data complete using simple empirical frequencies appropriate however trying produce classifier alternative approaches often work better many based optimizing conditional likelihood rather joint likelihood see particular structural extension logistic regression discriminative parameter learning belief net classifiers httpwwwcsualbertacagreinerresearchelr references therein issues applies learning best structure goal discriminative system many standard measures used structure learners inappropriate see discriminative model selection belief net structures httpwwwcsualbertacagreinerbnresultshtmlcriteriadisc cheers russ r greiner phone 780 4925461 dept computing science fax 780 4921071 alberta ingenuity centre machine learning university alberta email greinercsualbertaca edmonton ab t6g 2e8 canada httpwwwcsualbertacagreiner qiang ji writes hi interested references learning parameters even structure bayesian network combining training data qualitative knowledge extracted domain experts thanks qiang ji phd associate professor director intelligent systems lab dept electrical computer systems eng rensselaer polytechnic institute troy ny 12180 httpwwwecserpieduqji uai mailing list rf mark subject uai rf rules clean localtest oktest ok thu 07 aug 2008 155928 0600 uai learning bn qualitative knowledge