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This paper proposes a novel multi-class cluster support vector machine, which borrows ideas of nonparallel hyperplanes from generalized eigenvalue support vector machines. For a k-class classification problem, it trains k nonparallel hyperplanes respectively, and each one lies as close as possible to self-class while apart from the rest classes as far as possible. Then, the label of a new sample is...
Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a class separability criterion is developed in a high-dimensional kernel space, and feature selection is performed by the maximization of this criterion...
In head MRI image sequences, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional 3D modeling algorithms. Support vector machine (SVM) based on statistical learning theory has solid theoretical foundation. sphere-shaped SVM (SSSVM) was originally developed for solving some special classification problems. In this paper, it is extended to...
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