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As a new version of support vector machine (SVM), least squares SVM (LS-SVM) involves equality instead of inequality constraints and works with a least squares cost function. A well-known drawback in the LS-SVM applications is that the sparseness is lost. In this paper, we develop an adaptive pruning algorithm based on the bottom-to-top strategy, which can deal with this drawback. In the proposed...
Support vector machines (SVMs) are originally designed for binary classifications. As for multi-classifications, they are usually converted into binary ones. In the conventional multi-classifiable algorithms, One-against-One algorithm is a very power method. However, there exists a middle unclassifiable region. In order to overcome this drawback, a novel method called Nesting Algorithm is presented...
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