Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how ...

This paper presents a new approach to rule extraction from Support Vector Machines (SVMs). SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary through querying followed by clustering, searching and then to extract rules by solving an optimizati...

We describe Hidden Semi-Markov Support Vector Machines (SHM SVMs), an extension of HM SVMs to semi-Markov chains. This allows us to predict segmentations of sequences based on segment-based features measuring properties such as the length of the segment. We propose a novel technique to partition the problem into sub-problems. The independently obtained partial solutions can then be recombined i...

Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as ElectroEncephaloGrams (EEGs), is extremely challenging. We propose a novel Multi-Dimensional Time Series (MDTS) classification technique, called Connectivity Support Vector Machines (C-SVMs) that integrates brain connectivity network with SVMs. To alter noise in EEG data, Independent Component Analys...

Support Vector Machines (SVMs) is basically a discriminative classifiers, while it is hopefully that incorporating probability into SVMs will achieve better performance. This paper briefly reviews some of the methods that can be used to carry out the combination. By following one of them, we make it suitable for the task of speaker recognition, and Gaussian Mixture Models (GMM) is used as the g...

We describe a method for solving large-scale semiparametric support vector machines (SVMs) for regression problems. Most of the approaches proposed to date for large-scale SVMs cannot accommodate the multiple equality constraints that appear in semiparametric problems. Our approach uses a decomposition framework, with a primal-dual algorithm to find an approximate saddle point for the min-max f...

Support Vector Machines (SVMs) have become a popular learning algorithm, in particular for large, high-dimensional classification problems. SVMs have been shown to give most accurate classification results in a variety of applications. Several methods have been proposed to obtain not only a classification, but also an estimate of the SVMs confidence in the correctness of the predicted label. In...

With the adoption of min-max-modular support vector machines (SVMs) to solve large-scale patent classification problems, a novel, simple method for incorporating prior knowledge into task decomposition is proposed and investigated. Two kinds of prior knowledge described in patent texts are considered: time information, and hierarchical structure information. Through experiments using the NTCIR-...

We discuss sparse support vector machines (SVMs) by selecting the linearly independent data in the empirical feature space. First we select training data that maximally separate two classes in the empirical feature space. As a selection criterion we use linear discriminant analysis in the empirical feature space and select training data by forward selection. Then the SVM is trained in the empir...

In this paper, we address the problem of dealing with a large collection of data and propose a method for text classification which manipulates data using two well-known machine learning techniques, Naive Bayes(NB) and Support Vector Machines(SVMs). NB is based on the assumption of word independence in a text, which makes the computation of it far more efficient. SVMs, on the other hand, have t...