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...

The Support Vector Machine is a theoretically superior machine learning methodology with great results in classification of highdimensional datasets and has been found competitive with the best machine learning algorithms. In the past, SVMs have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based techniques towards object-based representation, the dimensions...

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...

Soil moisture content (SMC) is an important parameter that affects tea growth. Reasonable soil improves quality and ensures yield. Therefore, it necessary to regularly monitor the water content. However, traditional prediction algorithm has problems of low accuracy efficiency. This paper constructs evaluates performance a hybrid arithmetic optimization (AOA) support vector machine (SVM) model (...

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...