For the present work, we attempt to study the issue of automatic acquisition of intonational phrase breaks. A mathematically well-formed framework is suggested, which is based on Bayesian theory. Based on two different assumptions regarding the conditional independence of the input attributes, we have come up with two Bayesian implementations, namely the Naïve Bayes and the Bayesian Networks cl...

For the present paper, a Bayesian probabilistic framework for the task of automatic acquisition of intonational phrase breaks was established. By considering two different conditional independence assumptions, the naïve Bayes and Bayesian networks approaches were regarded and evaluated against the CART algorithm, which has been previously used with success. A finite length window of minimal mor...

It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian a lgorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4....

The problem of title generation involves finding the essence of a document and expressing it in only a few words. The results of a query to the Informedia Digital Video Library are summarized through an automatically generated title for each retrieved news story. When the document is errorful, as with speech-recognized broadcast news stories, the title creation challenge becomes even greater. W...

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect ...

Cancer detection is one of the important research topics in medical science. In bioinformatics age, gene expression data can be used for the cancer detection. Data mining techniques, such as pattern association, classification and clustering, are now frequently applied in cancer and gene expressions correlation studies. Classification is very important among these techniques of data mining. Her...

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect f...

In this paper, we compare and contrast two methods for revising qualitative (viz., “full”) beliefs. The first method is a naïve Bayesian one, which operates via conditionalization and the minimization of expected inaccuracy. The second method is the AGM approach to belief revision. Our aim here is to provide the most straightforward explanation of the ways in which these two methods agree and d...

Correct identification of ethnicity is central to many epidemiologic analyses. Unfortunately, ethnicity data are often missing. Successful classification typically relies on large databases (n > 500,000 names) of known name-ethnicity associations. We propose an alternative naïve Bayesian strategy that uses substrings of full names. Name and ethnicity data for Malays, Indians, and Chinese were p...