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Fall 2010
Performance and stability of many iterative algorithms such as stochastic gradient descent largely depend on a fixed and scalar step-size parameter. Use of a fixed and scalar step-size value may lead to limited performance in many problems. We study several existing step-size adaptation...
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Spring 2013
Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space, MKL can be considered as a systematic approach to feature selection. Many of the existing MKL algorithms perform kernel learning...