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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...
Distance metric learning has exhibited its great power to enhance performance in metric related pattern recognition tasks. The recent large margin nearest neighbor classification (LMNN) improves the performance of k-nearest neighbor classification by learning a global distance metric. However, it does not consider the locality of data distributions, which is crucial in determining a proper metric...
The semi-supervised classification problem with partially labeled data is very important in the research area of pattern recognition and machine learning. In this paper, an approach based on transduction of labeled data is proposed to improve current classification methods. The general knowledge about the attribute of data distribution is used to carry out transduction. Employing this kind of knowledge,...
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