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Real AdaBoost With Gate Controlled Fusion

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3 Author(s)
Mayhua-Lopez, E. ; Dept. of Signal Theor. & Commun., Univ. Carlos III de Madrid, Leganes, Spain ; Gomez-Verdejo, V. ; Figueiras-Vidal, A.R.

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.

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Neural Networks and Learning Systems, IEEE Transactions on  (Volume:23 ,  Issue: 12 )