On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes Export

@misc{citeulike:1592848, abstract = {We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is increased, one in which each algorithm does better. This stems from the observation -- which is borne out in repeated experiments -- that while discriminative learning has lower asymptotic error, a...}, author = {Ng, A. and Jordan, M.}, citeulike-article-id = {1592848}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9829}, keywords = {learning, machine}, posted-at = {2007-09-14 04:14:46}, priority = {2}, title = {On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9829}, year = {2002} }

TY - GEN ID - citeulike:1592848 L3 - citeulike-article-id:1592848 N2 - We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is increased, one in which each algorithm does better. This stems from the observation -- which is borne out in repeated experiments -- that while discriminative learning has lower asymptotic error, a... TI - On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes KW - learning KW - machine AU - Ng, A AU - Jordan, M PY - 2002/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9829 ER -