Robust boosting for learning from few examples

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 359 - 364 vol. 1
• Main Authors: Wolf, L., Martin, I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Biology computing; Boosting; Iterative algorithms; Object detection; Robustness; Runtime; Support vector machine classification; Support vector machines; Testing; Time measurement
• DOI: 10.1109/CVPR.2005.305
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919

We present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of our original data. The motivation is that since the learning algorithm lacks information about which parts of the data are reliable, it has to make more robust classification functions. Using this framework, we propose a simple addition to the gentle boosting algorithm which enables it to work with only a few examples. We test this new algorithm on a variety of datasets and show convincing results.