Real adaboost with gate controlled fusion

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Date
2012
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Publisher
Scopus
Abstract
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. © 2012 IEEE.
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Keywords
Bench-mark problems , Classification performance , Computational effort , Controlled fusion , ensembles , Linear combinations , Mixtures of experts , Training requirement , Benchmarking , Classification (of information) , Direct energy conversion , Neural networks , Adaptive boosting
Citation
URI
https://hdl.handle.net/20.500.12590/15881
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Artículos - Ingeniería Electrónica y de Telecomunicaciones