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Naive Bayes classifier

Known as: Bayesian Classifiers, Naive-Bayes, Naïve Bayesian classification 
In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive… Expand
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Papers overview

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Highly Cited
2010
Highly Cited
2010
In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to… Expand
Highly Cited
2007
Highly Cited
2007
A basic assumption in traditional machine learning is that the training and test data distributions should be identical. This… Expand
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Highly Cited
2005
Highly Cited
2005
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and… Expand
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Highly Cited
2004
Highly Cited
2004
Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its… Expand
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Highly Cited
2003
Highly Cited
2003
Naive Bayes is often used as a baseline in text classification because it is fast and easy to implement. Its severe assumptions… Expand
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Highly Cited
2001
Highly Cited
2001
We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a… Expand
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Highly Cited
2001
Highly Cited
2001
The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence… Expand
Highly Cited
2001
Highly Cited
2001
The naive Bayesclassifiergreatly simplify learning byassumingthatfeaturesareindependent given class. Although independenceis… Expand
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Review
1998
Review
1998
The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in… Expand
Highly Cited
1996
Highly Cited
1996
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the… Expand
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