Por favor, use este identificador para citar o enlazar este ítem:
http://repositoriodigital.ipn.mx/handle/123456789/14767
Título : | Sparse and Non-Sparse Multiple Kernel Learning for Recognition |
Otros títulos : | Aprendizaje de múltiples núcleos esparcidos y no esparcidos para reconocimiento |
Autor : | Alioscha-Pérez, Mitchel Sahli, Hichem González, Isabel Taboada-Crispi, Alberto |
Palabras clave : | Keywords. Multiple kernel learning, object state recognition, norm regularizers, analytical updates, cutting plane method, Newton’s method. |
Fecha de publicación : | 5-jun-2012 |
Editorial : | Revista Computación y Sistemas; Vol. 16 No. 2 |
Citación : | Revista Computación y Sistemas; Vol. 16 No. 2 |
Citación : | Revista Computación y Sistemas;Vol. 16 No. 2 |
Resumen : | Abstract. 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. |
URI : | http://www.repositoriodigital.ipn.mx/handle/123456789/14767 |
ISSN : | 1405-5546 |
Aparece en las colecciones: | Revistas |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
167_Art. 3_.pdf | 625.53 kB | Adobe PDF | Visualizar/Abrir |
Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.