Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier
with strong assumptions of independence among features, called naive Bayes, is …

The simple Bayesian classifier is known to be optimal when attributes are independent
given the class, but the question of whether other sufficient conditions for its optimality exist …

When modeling a probability distribution with a Bayesian network, we are faced with the
problem of how to handle continuous variables. Most previous work has either solved the …

Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on
many classification tasks even when the conditional independence assumption on which …

In this paper, we examine previous work on the naive Bayesian classifier and review its
limitations, which include a sensitivity to correlated features. We respond to this problem by …

We have compared four classifiers on the problem of predicting the cellular localization sites
of proteins in yeast and E. coli. A set of sequence derived features, such as regions of high …

Despite its simplicity, the naive Bayes learning scheme performs well on most classification
tasks, and is often significantly more accurate than more sophisticated methods. Although …

The naive Bayesian classifier provides a simple and effective approach to classifier learning,
but its attribute independence assumption is often violated in the real world. A number of …

This paper introduces a new method, called the robust Bayesian estimator (RBE), to learn
conditional probability distributions from incomplete data sets. The intuition behind the RBE …

In supervised machine learning, some algorithms are restricted to discrete data and have to
discretize continuous attributes. Many discretization methods, based on statistical criteria …