The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives exp...

We introduce a robust classification method using Bayesian predictive distribution (Bayesian predictive classification, referred to as BPC) into speech recognition. We and others have recently proposed a total Bayesian framework for speech recognition, Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes an analytical derivation of approximate posterior di...

The seismic response of geological reservoirs is a function the elastic properties porous rocks, which depends on rock types, petrophysical features, and environments. Such characteristics are generally classified into facies. We propose to use convolutional neural networks in Bayesian framework predict facies based data quantify uncertainty classification. A variational approach adopted approx...

We introduce a conservative fuzzy logic extension of the Naive Bayesian classification algorithm. The extension generalizes the algorithm such that the examples are described by a fuzzy set of attributes, instead of a classical set. Thus, an example possesses each attribute to a degree in [0, 1]. We present a new classification algorithm usable with fuzzy sets that is (a) fast, (b) is able to w...

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...