In this work, we make a contribution to natural speech dialogue act detection. We focus our attention on the dialogue act classification using a Bayesian approach. Our classifier is tested on two corpora, the Switchboard and the Basurde tasks. A combination of a naive Bayes classifier and n-grams is used. The impact of different smoothing methods (Laplace and Witten Bell) and n-grams in classif...

We use abstract algebra to derive new algorithms for fast cross-validation, online learning, and parallel learning. To use these algorithms on a classification model, we must show that the model has appropriate algebraic structure. It is easy to give algebraic structure to some models, and we do this explicitly for Bayesian classifiers and a novel variation of decision stumps called HomStumps. ...

The naive Bayesian classifier is a simple and effective classification method, which assumes a Bayesian network in which each attribute has the class label as its only one parent. But this assumption is not obviously hold in many real world domains. Tree-Augmented Naive Bayes (TAN) is a state-of-the-art extension of the naive Bayes, which can express partial dependence relations among attribute...

Discriminative classifiers such as Support Vector Machines (SVM) directly learn a discriminant function or a posterior probability model to perform classification. On the other hand, generative classifiers often learn a joint probability model and then use the Bayes rule to construct a posterior classifier. In general, generative classifiers are not as accurate as discriminative classifiers. Ho...

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...

In this paper an attempt has been made to develop classification algorithm for remotely sensed satellite data using Bayesian and hybrid classification approach. Bayesian classification is a probabilistic technique which is capable of classifying every pattern until no pattern remains unclassified. Hybrid classification involves developing training patterns using unsupervised classification foll...

While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become as widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of com...

We present a framework for characterizing Bayesian classification methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning of full Bayesian networks at the most general extreme. While much work has been carried out along the two ends of this spectrum, there has been ...