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In this paper, we present a new classification method named multitask multiclass support vector machines based on the regularization principle. Our starting point is the recent success of multitask learning which has shown that learning multiple related tasks simultaneously can get better results than learning these tasks independently. We cast multitask multiclass problems as a constrained optimization...
Feature selection for ensembles can often improve generalization accuracy of classifiers. In this paper we present a strategy on the feature selection for ensembles based on a hierarchical Non-dominated Sorting in Genetic Algorithms (NSGA-II) proposed by Deb. The first level of our strategy performs feature selection in order to generate a set of good classifiers, the second one deletes redundant...
Support vector machines (SVMs) are effective kernel methods to solve pattern recognition problems. Traditionally, they adopt a single kernel chosen beforehand, which makes them lack flexibility. The recent multiple kernel learning (MKL) overcomes this issue by optimizing over a linear combination of kernels. Despite its success, MKL neglects useful information generated from the nonlinear interaction...
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