A Bayesian procedure for the simultaneous alignment and classification of sequences into subclasses is described. This Gibbs sampling algorithm iterates between an alignment step and a classification step. It employs Bayesian inference for the identification of the number of conserved columns, the number of motifs in each class, their size, and the size of the classes. Using Bayesian prediction...

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in better classification accuracy. In previous work [1], we have introduced a new type of Bayesian classifier called Case-Based Bayesian Network (CBBN) classifiers. We have shown that CBBNs can capture finer levels of semantics than possible in traditional Bayesian Networks (...

In this work the analysis of branches of Ukrainian economy was done, particularly average financial parameters were found. For each parameter the boundaries were determined which divide enterprises into 5 parts and allow making more detailed ratings. The ratings were made by each parameter and then the aggregate rating was found. The analysis of indices interrelation was made using Bayesian net...

The simple Bayesian classi er (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and ca...

Negotiation is one of the most fundamental and effective mechanism for resolving conflicts between self-interested agents and producing mutually acceptable compromises. Most existing research in negotiation presumes a fixed negotiation context which cannot be changed during the process of negotiation and that the agents have complete and correct knowledge about all aspects of the issues being n...

The Bayesian classifier is a simple approach to classification that produces results that are easy for people to interpret. In many cases, the Bayesian classifier is at least as accurate as much more sophisticated learning algorithms that produce results that are more difficult for people to interpret. To use numeric attributes with Bayesian classifier often requires the attribute values to be ...

The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning. 2004 Elsevier B.V. All rights reserved.

Quadratic discriminant analysis is a common tool for classification, but estimation of the Gaussian parameters can be ill-posed. This paper contains theoretical and algorithmic contributions to Bayesian estimation for quadratic discriminant analysis. A distribution-based Bayesian classifier is derived using information geometry. Using a calculus of variations approach to define a functional Bre...

Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge t...