recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

<p style='text-indent:20px;'>We propose <inline-formula><tex-math id="M1">\begin{document}$ \ell_1 $\end{document}</tex-math></inline-formula> norm regularized quadratic surface support vector machine models for binary classification in supervised learning. We establish some desired theoretical properties, including the existence and uniqueness of optimal solution,...

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-cla...

We consider parallel decomposition techniques for solving the large quadratic programming (QP) problems arising in training support vector machines. A recent technique is improved by introducing an efficient solver for the inner QP subproblems and a preprocessing step useful to hot start the decomposition strategy. The effectiveness of the proposed improvements is evaluated by solving large-sca...

Gradient projection methods based on the Barzilai-Borwein spectral steplength choices are considered for quadratic programming problems with simple constraints. Well-known nonmonotone spectral projected gradient methods and variable projection methods are discussed. For both approaches the behavior of different combinations of the two spectral steplengths is investigated. A new adaptive steplen...

This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic programming problem that can be solved by adap...

This article employs Least Square Support Vector Machine (LSSVM) for determination of Compression Index (Cc) of marine clay in east coast of Korea. This study uses LSSVM as a regression tool. In LSSVM, the regression equation is obtained as the solution to a linear system instead of a quadratic programming (QP) problem. The input parameters of LSSVM are natural water content (n), liquid limit ...

Sphere structured support vector machine is a multi-classification algorithm. The algorithm separately constructs sphere for each class sample data, so the complex degree of the quadratic programming is reduced and it is easier to extend new samples. But, kernel parameter selection of sphere structured support vector machine needs to predetermine the parameter search interval. For eliminating h...

In this work, we developed a method to efficiently optimize the kernel function for combined data of various different sources with their corresponding kernels being already available. The vectorization of the combined data is achieved by a weighted concatenation of the existing data vectors. This induces a kernel matrix composed of the existing kernels as blocks along the main diagonal, weight...