A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Multiclass learning, boosting, and error-correcting codes
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Reducing multiclass to binary: a unifying approach for margin classifiers
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If one has a multiclass classification problem and wants to boost a multiclass base classifier AdaBoost.M1 is a well known and widely applicated boosting algorithm. However AdaBoost.M1 does not work, if the base classifier is too weak. We show, that with a modification of only one line of AdaBoost.M1 one can make it usable for weak base classifiers, too. The resulting classifier AdaBoost.M1W is guaranteed to minimize an upper bound for a performance measure, called the guessing error, as long as the base classifier is better than random guessing. The usability of AdaBoost.M1W could be clearly demonstrated experimentally.