Combinations of weak classifiers
- 1 January 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 8 (1) , 32-42
- https://doi.org/10.1109/72.554189
Abstract
— To obtain classification systems with both good generalizat` ıon performance and efficiency in space and time, we propose a learning method based on combinations of weak clas- sifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random,guesses. A ran- domized algorithm is proposed to find the weak classifiers. They are then combined,through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity. Index Terms— Weak classifiers, combinations of classifiers, su-Keywords
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