Real adaboost with gate controlled fusion

Efrain Tito Mayhua Lopez, Vanessa Gomez-Verdejo, Aníbal R. Figueiras-Vidal

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

10 Citas (Scopus)


In this brief, we propose to increase the capabilities of standard real AdaBoost (RAB) architectures by replacing their linear combinations with a fusion controlled by a gate with fixed kernels. Experimental results in a series of well-known benchmark problems support the effectiveness of this approach in improving classification performance. Although the need for cross-validation processes obviously leads to higher training requirements and more computational effort, the operation load is never much higher; in many cases it is even lower than that of competitive RAB schemes.

Idioma originalInglés
Número de artículo6352922
Páginas (desde-hasta)2003-2009
Número de páginas7
PublicaciónIEEE Transactions on Neural Networks and Learning Systems
EstadoPublicada - 2012
Publicado de forma externa

Nota bibliográfica

Funding Information:
Manuscript received July 5, 2011; revised September 10, 2012; accepted September 10, 2012. Date of publication November 10, 2012; date of current version November 20, 2012. This work was supported in part by the Spanish MICINN under Grant TEC 2011-22480, Grant TIN 2011-24533, and Grant PRI-PIBIN 2011-1266.


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