TY - GEN

T1 - Boosting versus covering

AU - Hatano, Kohei

AU - Warmuth, Manfred K.

PY - 2004/1/1

Y1 - 2004/1/1

N2 - We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In particular, for any set of m labeled examples consistent with a disjunction of literals (which are one-sided in this case), AdaBoost constructs a consistent hypothesis by using O(2 logm) iterations. On the other hand, a greedy set covering algorithm finds a consistent hypothesis of size O( logm). Our primary question is whether there is a simple boosting algorithm that performs as well as the greedy set covering. We first show that InfoBoost, a modification of AdaBoost proposed by Aslam for a different purpose, does perform as well as the greedy set covering algorithm. We then show that AdaBoost requires Ω(2 logm) iterations for learning -literal disjunctions. We achieve this with an adversary construction and as well as in simple experiments based on artificial data. Further we give a variant called SemiBoost that can handle the degenerate case when the given examples all have the same label. We conclude by showing that SemiBoost can be used to produce small conjunctions as well.

AB - We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In particular, for any set of m labeled examples consistent with a disjunction of literals (which are one-sided in this case), AdaBoost constructs a consistent hypothesis by using O(2 logm) iterations. On the other hand, a greedy set covering algorithm finds a consistent hypothesis of size O( logm). Our primary question is whether there is a simple boosting algorithm that performs as well as the greedy set covering. We first show that InfoBoost, a modification of AdaBoost proposed by Aslam for a different purpose, does perform as well as the greedy set covering algorithm. We then show that AdaBoost requires Ω(2 logm) iterations for learning -literal disjunctions. We achieve this with an adversary construction and as well as in simple experiments based on artificial data. Further we give a variant called SemiBoost that can handle the degenerate case when the given examples all have the same label. We conclude by showing that SemiBoost can be used to produce small conjunctions as well.

UR - http://www.scopus.com/inward/record.url?scp=84860966920&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860966920&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84860966920

SN - 0262201526

SN - 9780262201520

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003

PB - Neural information processing systems foundation

T2 - 17th Annual Conference on Neural Information Processing Systems, NIPS 2003

Y2 - 8 December 2003 through 13 December 2003

ER -