Randomized rounding: a technique for provably good algorithms and algorithmic proofs
Combinatorica - Theory of Computing
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
The Strength of Weak Learnability
Machine Learning
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
From noise-free to noise-tolerant and from on-line to batch learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Error reduction through learning multiple descriptions
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Improved Generalization Through Explicit Optimization of Margins
Machine Learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Some Elements of Machine Learning (Extended Abstract)
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
Motivated by a theoretical analysis of the generalization of boosting, we examine learning algorithms that work by trying to fit data using a simple majority vote over a small number of a collection of hypotheses. We provide experimental evidence that an algorithm based on this principle outputs hypotheses that often generalize nearly as well as those output by boosting, and sometimes better. We also provide experimental evidence for an additional reason that boosting algorithms generalize well, that they take advantage of cases in which there are many simple hypotheses with independent errors.