Reference : Learning parameters in discrete naive Bayes models by computing fibers of the paramet...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/34058
Learning parameters in discrete naive Bayes models by computing fibers of the parametrization map
English
Auvray, Vincent [ > > ]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
20-Dec-2008
NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning
Yes
International
NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning
[en] Machine Learning ; Graphical Models
[en] Discrete Naive Bayes models are usually defined parametrically with a map from a parameter space to a probability distribution space. First, we present two families of algorithms that compute the set of parameters mapped to a given discrete Naive Bayes distribution satisfying certain technical assumptions. Using these results, we then present two families of parameter learning algorithms that operate by projecting the distribution of observed relative frequencies in a dataset onto the discrete Naive Bayes model considered. They have nice convergence properties, but their computational complexity grows very quickly with the number of hidden classes of the model.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/34058
Video lecture and slides available at http://videolectures.net/aml08_auvray_lpdnbmcfpm/

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