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In the field of machine learning, it is a key issue to learn and represent similarity. This paper focuses on the problem of learning similarity with a multikernel method. Motivated by geometric intuition and computability, similarity between patterns is proposed to be measured by their included angle in a kernel-induced Hilbert space. Having noticed that the cosine of such an included angle can be...
Abstract-In this paper, the extension work on the performance of l1-regularized support vector machine(l1-svm) from the classical independent and identically distributed input sequence to the stationary β-mixing input sequence is considered. We establish the bound of generalization error for the l1-mixing stationary sequence. It is interesting that our result is available even the size of the dictionary...
This paper considers the regularized learning algorithm associated with the least-square loss and compressed domain. The target is the error analysis for the regression problem learned in compressed domain. We show that the least-square regularized algorithm is beneficial from the compressed sensing.
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