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Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
Taking motivation from Twin Support Vector Machine (TWSVM), Peng (2009) attempted to propose Twin Support Vector Regression (TSVR) where regressor was obtained via solving pair of Quadratic Programming Problems(QPPs). However the discussed formulation was not on the lines of TWSVM and had some restrictions. In this paper we propose formulation termed as Twin Support Vector Machine based Regression(TWSVR)...
In this paper, base on the nonparallel hyper plane classifier, v-Nonparallel Support Vector Machine (NPSVM), we proposed its linear programming formulation, termed as v - LPNPSVM. v-NPSVM which has been proved superior to the twin support vector machines (TWSVMs), is parameterized by the quantity v to let ones effectively control the number of Support Vectors. Compared with the quadratic programming...
We present a new decomposition algorithm for training bound-constrained Support Vector Machines in this paper. When selecting indices into the working set, only first order derivative information of the objective function in the optimization model is required. Therefore, the resulting working set selection strategy is simple and can be implemented easily. The new algorithm is proved to be global convergent...
Developing learning algorithms for multilabel classification problems, when the goal is to maximizing the micro-averaged F measure, is a difficult problem for which no solution was known so far. In this paper we provide an exact solution for the case when the popular binary relevance approach is used for designing a multilabel classifier. We prove that the empirical maximum of the micro-averaged F...
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