alexa On Dis riminative vs. Generative lassi ers: A omparison of logisti regression and naive Bayes
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Andrew Y Ng, Mi hael I Jordan

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We ompare dis riminative and generative learning as typi ed by logisti regression and naive Bayes. We show, ontrary to a widelyheld belief that dis riminative lassi ers are almost always to be preferred, that there an often be two distin t regimes of performan e as the training set size is in reased, one in whi h ea h algorithm does better. This stems from the observation|whi h is borne out in repeated experiments|that while dis riminative learning has lower asymptoti error, a generative lassi er may also approa h its (higher) asymptoti error mu h faster.

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This article was published in Advances in neural information processing systems and referenced in International Journal of Biomedical Data Mining

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