We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as suppo...

We develop a Bayesian uncertainty quantification framework using a local binary tree surrogate model that is able to make use of arbitrary Bayesian regression methods. The tree is adaptively constructed using information about the sensitivity of the response and is biased by the underlying input probability distribution. The local Bayesian regressions are based on a reformulation of the relevan...

We address the problem of featureless patternrecognition under the assumption that pair-wise comparison of objects is arbitrarily scored by real numbers. Such a linear embedding is much more general than the traditional kernel-based approach, which demands positive semi-definiteness of the matrix of object comparisons. This demand is frequently prohibitive and is further complicated if there ex...

The Relevance Vector Machine (RVM) introduced by Tipping is a probabilistic model similar to the widespread Support Vector Machines (SVM), but where the training takes place in a Bayesian framework, and where predictive distributions of the outputs instead of point estimates are obtained. In this paper we focus on the use of RVM’s for regression. We modify this method for training generalized l...

Relevance vector machines (RVM) is a family of machine learning methods, introduced by Tipping, that represent a bayesian approach to the training of general linear models (GLM). RVM-s are reported to be able to e ectively produce convenient sparse representations of data thus competing with the popular support vector machines. When used with a suitable set of basis functions, RVM-s can be appl...

This paper introduces the use of Relevance Vector Machines (RVMs) for content based image classification and compares it with the conventional Support Vector Machine (SVM) approach. Different wavelet kernels are included in the formulation of the RVM. We also propose a new wavelet based feature extraction method that extracts lesser number of features as compared to other wavelet based feature ...

structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. biggest class of the repetitive subsequences is “transposable elements” which has its own sub-classes upon contexts’ structures. many researches have been performed to criticality determine the structure and function of repetitive su...

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to t...