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Machine learning / Naive Bayes classifier / Multiclass classification / Linear classifier / Document classification / Classification rule / Random naive Bayes / Averaged one-dependence estimators / Statistics / Statistical classification / Bayesian statistics


Tackling the Poor Assumptions of Naive Bayes Text Classifiers
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Document Date: 2006-01-11 19:03:07

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File Size: 231,85 KB

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San Francisco / Cambridge / Washington DC / Boston / /

Company

Artificial Intelligence Laboratory / Microsoft / Reuters / Lewis D. D. / Wiley and Sons Inc. / MIT Oxygen Partnership / /

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Product Recall / Product Issues / /

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Massachusetts Institute of Technology / /

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power-law-like term distributions / power-law distribution / stateof-the-art text classification algorithms / pre-processing steps / heuristic solution / Less erroneous algorithms / power law / pre-processing system / stateof-the-art text classification algorithm / power law distribution / power law distributions / heuristic solutions / /

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National Science Foundation / IDF / Massachusetts Institute of Technology / /

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David R. Karger / Duda / Hart / Jason D. M. Rennie / Jaime Teevan / Lawrence Shih / /

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Skullcandy G.I. Headphone/Headset / breakeven / Naive Bayes / SvmFu1 / /

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Massachusetts / /

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Machine Learning / /

Technology

artificial intelligence / Less erroneous algorithms / stateof-the-art text classification algorithms / Machine Learning / stateof-the-art text classification algorithm / /

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