Novel Weak Classfier Boosting Algorithm
This project was devoted to investigation of modern classification
techniques and their possible application to computer vision tasks.
During the research we have devised a new classification algorithm,
based on weak classifier boosting approach. Our method, called
"Modest AdaBoost", was implemented in MatLab enviroment and
compared to well known Gentle AdaBoost scheme. Our experiments,
conducted on UCI Machine Learning Repository database sets using
5-fold cross validation, show that our algorithm provides:
- Better generalization capabilities;
- Higher robustness to overfitting;
- Natural Stopping criterion;
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| Test error comparison of Gentle and Modest AdaBoost |
Publications
Alexander Vezhnevets, Vladimir Vezhnevets
"'Modest AdaBoost' - Teaching AdaBoost to Generalize Better".
Graphicon-2005, Novosibirsk Akademgorodok, Russia, 2005.
.pdf (107kb)
The project team
Dr. Vladimir Vezhnevets
Alexander Vezhnevets
Contacts
Vezhnevets Alexander
<vezhnick @ gmail.com>
(remove spaces from the address before sending).
Vezhnevets Vladimir
<vvp @ graphics.cs.msu.ru>
(remove spaces from the address before sending).
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