Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging
Abstract
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.
Cite
Text
Koltchinskii et al. "Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging." Neural Information Processing Systems, 2004.Markdown
[Koltchinskii et al. "Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/koltchinskii2004neurips-optimal/)BibTeX
@inproceedings{koltchinskii2004neurips-optimal,
title = {{Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging}},
author = {Koltchinskii, Vladimir and Martínez-ramón, Manel and Posse, Stefan},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {705-712},
url = {https://mlanthology.org/neurips/2004/koltchinskii2004neurips-optimal/}
}