Abstract
We describe a novel adaptive method that achieves robustness in pattern classification by combining a large number of weak classifiers. The individual classifiers are trained on subsets of features of the training samples and the output classification is obtained by a weighted sum of the individual weak classifiers. When the classifier is applied to the test set, the combination weights are adaptively adjusted in accordance with the agreement among the individual classifiers. We evaluated the performances of several different combination methods using simulated data and the results proved to be robust.
Original language | English (US) |
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Pages | 2272-2276 |
Number of pages | 5 |
State | Published - 2003 |
Event | International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: Jul 20 2003 → Jul 24 2003 |
Other
Other | International Joint Conference on Neural Networks 2003 |
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Country/Territory | United States |
City | Portland, OR |
Period | 7/20/03 → 7/24/03 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence