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A theory of multiclass boosting

Author(s): Mukherjee, Indraneel; Schapire, Robert E

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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.
Publication Date: Feb-2013
Citation: Mukherjee, Indraneel, and Robert E. Schapire. "A theory of multiclass boosting." The Journal of Machine Learning Research 14, no. 1 (2013): 437-497.
ISSN: 1532-4435
EISSN: 1533-7928
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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