Communications of the ACM
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
The Strength of Weak Learnability
Machine Learning
An improved boosting algorithm and its implications on learning complexity
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Boosting a weak learning algorithm by majority
Information and Computation
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
An adaptive version of the boost by majority algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
More efficient PAC-learning of DNF with membership queries under the uniform distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Hard-core distributions for somewhat hard problems
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
On agnostic boosting and parity learning
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Smooth Boosting for Margin-Based Ranking
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Learning Halfspaces with Malicious Noise
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Learning Halfspaces with Malicious Noise
The Journal of Machine Learning Research
Approximate reduction from AUC maximization to 1-norm soft margin optimization
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Smooth boosting using an information-based criterion
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning tasks whose solutions use a boosting technique. The boosting approach was originally suggested for the standard PAC model; we analyze possible applications of boosting in the context of agnostic learning, which is more realistic than the PAC model. We derive a lower bound for the final error achievable by boosting in the agnostic model and show that our algorithm actually achieves that accuracy (within a constant factor). We note that the idea of applying boosting in the agnostic model was first suggested by Ben-David, Long and Mansour (2001) and the solution they give is improved in the present paper. The accuracy we achieve is exponentially better with respect to the standard agnostic accuracy parameter β. We also describe the construction of a boosting "tandem" whose asymptotic number of iterations is the lowest possible (in both γ and ε and whose smoothness is optimal in terms of Õ(·). This allows adaptively solving problems whose solution is based on smooth boosting (like noise tolerant boosting and DNF membership learning), while preserving the original (non-adaptive) solution's complexity.