TY - GEN
T1 - A theory of multiclass boosting
AU - Mukherjee, Indraneel
AU - Schapire, Robert E.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85162059054&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85162059054&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85162059054
SN - 9781617823800
T3 - Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
BT - Advances in Neural Information Processing Systems 23
PB - Neural Information Processing Systems
T2 - 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Y2 - 6 December 2010 through 9 December 2010
ER -