Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Vladimir Koltchinskii, Manel Martínez-ramón, Stefan Posse


We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss function under the assumption that the so- lution of optimal aggregation problem is sparse. We use a boosting type algorithm of optimal aggregation to develop aggregate classifiers of ac- tivation patterns in fMRI based on locally trained SVM classifiers. The aggregation coefficients are then used to design a "boosting map" of the brain needed to identify the regions with most significant impact on clas- sification.