Bayesian Network Classifiers Export

@article{citeulike:197223, abstract = {. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored...}, author = {Friedman, Nir and Geiger, Dan and Goldszmidt, Moises}, citeulike-article-id = {197223}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.9978}, journal = {Machine Learning}, keywords = {bayesian\_classifiers}, number = {2-3}, pages = {131--163}, posted-at = {2005-10-08 01:06:19}, priority = {2}, title = {Bayesian Network Classifiers}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.9978}, volume = {29}, year = {1997} }

TY - JOUR ID - citeulike:197223 L3 - citeulike-article-id:197223 N2 - . Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored... IS - 2-3 JF - Machine Learning EP - 163 TI - Bayesian Network Classifiers VL - 29 SP - 131 KW - bayesian_classifiers AU - Friedman, Nir AU - Geiger, Dan AU - Goldszmidt, Moises PY - 1997/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.9978 ER -