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NIPS
2004

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

9 years 15 days ago
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging
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 solution of optimal aggregation problem is sparse. We use a boosting type algorithm of optimal aggregation to develop aggregate classifiers of activation 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 classification.
Vladimir Koltchinskii, Manel Martínez-Ram&o
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Vladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse
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