The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the highprecision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of t...

IRIS flower data is a class of multi variable data set, which is widely applied in data classification. This paper aims at the parameter optimization problem of least squares support vector machine (LS-SVM) in data classification, an improved particle swarm optimization(IMPSO) algorithm is introduced into the LS-SVM model for improving the learning performance and generalization ability of LS-S...

Because the properties of data are becoming more and more complex, the traditional data classification is difficult to realize the data classification according to the complexity characteristic of the data. Support vector machine is a machine learning method with the good generalization ability and prediction accuracy. So an improved ant colony optimization(ACO) algorithm is introduced into the...

In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to eval...

We present a geometrically motivated algorithm for finding the Support Vectors of a given set of points. This algorithm is reminiscent of the DirectSVM algorithm, in the way it picks data points for inclusion in the Support Vector set, but it uses an optimization based approach to add them to the Support Vector set. This ensures that the algorithm scales to O(n) in the worst case and O(n|S|) in...

Support Vector Machine is one of the most classical approaches for classification and regression. Despite being studied for decades, obtaining practical algorithms for SVM is still an active research problem in machine learning. In this paper, we propose a new perspective for SVM via saddle point optimization. We provide an algorithm which achieves (1 − )-approximations with running time Õ(nd +...

We propose and study a new variant of the SVM — the SVM with uneven margins, tailored for document categorisation problems (i.e. problems where classes are highly unbalanced). Our experiments showed that the new algorithm significantly outperformed the SVM with respect to the document categorisation for small categories. Furthermore, we report the results of the SVM as well as our new algorithm...

We propose and study a new variant of the SVM — the SVM with uneven margins, tailored for document categorisation problems (i.e. problems where classes are highly unbalanced). Our experiments showed that the new algorithm significantly outperformed the SVM with respect to the document categorisation for small categories. Furthermore, we report the results of the SVM as well as our new algorithm...

A new decomposition algorithm for training regression Support Vector Machines (SVM) is presented. The algorithm builds on the basic principles of decomposition proposed by Osuna et. al ., and addresses the issue of optimal working set selection. The new criteria for testing optimality of a working set are derived. Based on these criteria, the principle of "maximal inconsistency" is proposed to ...

The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particle...