A FPGA-based parallel semi-naive Bayes classifier implementation

Bayes and Naive Bayes Classifier

Bayes and Naive-Bayes Classifier

Pairwise Naive Bayes Classifier

Semi-supervised Learning of Naive Bayes Classifier with feature constraints

Semi-supervised learning methods address the problem of building classifiers when labeled data is scarce. Text classification is often augmented by rich set of labeled features representing a particular class. As tuple level labling is resource consuming, semi-supervised and weakly supervised learning methods are explored recently. Compared to labeling data instances (documents), feature labeli...

The Indifferent Naive Bayes Classifier

The Naive Bayes classifier is a simple and accurate classifier. This paper shows that assuming the Naive Bayes classifier model and applying Bayesian model averaging and the principle of indifference, an equally simple, more accurate and theoretically well founded classifier can be obtained. Introduction In this paper we use Bayesian model averaging and the principle of indifference to derive a...

A Naive Bayes Style Possibilistic Classifier

Naive Bayes classifiers can be seen as special probabilistic networks with a star-like structure. They can easily be induced from a dataset of sample cases. However, as most probabilistic approaches, they run into problems, if imprecise (i.e, set-valued) information in the data to learn from has to be taken into account. An approach to handle uncertain as well imprecise information, which recen...