Resumo

ALIOSCHA-PEREZ, Mitchel; SAHLI, Hichem; GONZALEZ, Isabel  e  TABOADA-CRISPI, Alberto. Sparse and Non-Sparse Multiple Kernel Learning for Recognition. Comp. y Sist. [online]. 2012, vol.16, n.2, pp.167-174. ISSN 2007-9737.

The development of Multiple Kernel Techniques has become of particular interest for machine learning researchers in Computer Vision topics like image processing, object classification, and object state recognition. Sparsity-inducing norms along with non-sparse formulations promote different degrees of sparsity at the kernel coefficient level, at the same time permitting non-sparse combination within each individual kernel. This makes MKL models very suitable for different problems, allowing adequate selection of the regularizer according to different norms and the nature of the problem. We formulate and discuss MKL regularizations and optimization approaches, as well as demonstrate MKL effectiveness compared to the state-of-the-art SVM models using a Computer Vision Recognition problem.

Palavras-chave : Multiple kernel learning; object state recognition; norm regularizers; analytical updates; cutting plane method; Newton's method.

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