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|Abstract:||Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.|
|Citation:||Mukherjee, Indraneel, and Robert E. Schapire. "A theory of multiclass boosting." The Journal of Machine Learning Research 14, no. 1 (2013): 437-497.|
|Pages:||437 - 497|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Journal of Machine Learning Research|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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