TY - GEN
T1 - Regularizing AdaBoost
AU - Rätsch, Gunnar
AU - Onoda, Takashi
AU - Müller, Klaus R.
PY - 1999
Y1 - 1999
N2 - Boosting methods maximize a hard classification margin and are known as powerful techniques that do not exhibit overfitting for low noise cases. Also for noisy data boosting will try to enforce a hard margin and thereby give too much weight to outliers, which then leads to the dilemma of non-smooth fits and overfitting. Therefore we propose three algorithms to allow for soft margin classification by introducing regularization with slack variables into the boosting concept: (1) AdaBoostreg and regularized versions of (2) linear and (3) quadratic programming AdaBoost. Experiments show the usefulness of the proposed algorithms in comparison to another soft margin classifier: the support vector machine.
AB - Boosting methods maximize a hard classification margin and are known as powerful techniques that do not exhibit overfitting for low noise cases. Also for noisy data boosting will try to enforce a hard margin and thereby give too much weight to outliers, which then leads to the dilemma of non-smooth fits and overfitting. Therefore we propose three algorithms to allow for soft margin classification by introducing regularization with slack variables into the boosting concept: (1) AdaBoostreg and regularized versions of (2) linear and (3) quadratic programming AdaBoost. Experiments show the usefulness of the proposed algorithms in comparison to another soft margin classifier: the support vector machine.
UR - http://www.scopus.com/inward/record.url?scp=0001102148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0001102148&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0001102148
SN - 0262112450
SN - 9780262112451
T3 - Advances in Neural Information Processing Systems
SP - 564
EP - 570
BT - Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PB - Neural information processing systems foundation
T2 - 12th Annual Conference on Neural Information Processing Systems, NIPS 1998
Y2 - 30 November 1998 through 5 December 1998
ER -