Robust boosting for learning from few examples

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. Us...

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Bibliographic Details
Published in2005 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
LanguageEnglish
Published IEEE 2005
Subjects
Biology computing
Boosting
Iterative algorithms
Object detection
Robustness
Runtime
Support vector machine classification
Support vector machines
Testing
Time measurement
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Summary: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.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.305