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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

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

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

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

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

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

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