Boosting the minimum margin: LPBoost vs. AdaBoost
Date
2008
Authors
Li, Hanxi
Shen, Chunhua
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Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
LPBoost seemingly should have better generalization ca- pability than AdaBoost according to the margin theory [12] because LPBoost optimizes the minimum margin directly. Thus far, however, there is no empirical comparison and theoretical explanation of LPBoost against AdaBoost. We have conducted an experimental evaluation on the classi- fication performance of LPBoost and AdaBoost in this pa- per. Our results show that the LPBoost performs worse than AdaBoost in most cases. By considering the margin distri- bution, we present an explanation. Also, our finding indi- cates that besides the minimum margin, which is directly and globally optimized in LPBoost, the margin distribution plays a more important role in terms of the learned strong classifier's classification performance.
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Keywords
Keywords: AdaBoost; Classification performance; Empirical comparison; Experimental evaluation; Margin theory; Theoretical explanation; Adaptive boosting
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Source
Proceedings of Digital Image Computing: Techniques and Applications (DICTA 2008)
Type
Conference paper
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Restricted until
2037-12-31
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