Adaptive Kernel Matching Pursuit for Pattern Classification

A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the number of training examples increases. A recent proposed method, the Kernel Matching Pursuit, presents a number of advantages over the


Published in:
Proceedings of International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, 235-239
Year:
2004
Publisher:
IEEE
Keywords:
Laboratories:




 Record created 2006-06-14, last modified 2018-03-17

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