Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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 classifiers regionally
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Improved Generalization Through Explicit Optimization of Margins
Machine Learning
Corpus-based statistical screening for content-bearing terms
Journal of the American Society for Information Science and Technology
Machine Learning
Pattern Recognition Letters
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Improving Algorithms for Boosting
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Machine Learning
Web Semantics: Science, Services and Agents on the World Wide Web
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Schapire and Singer's improved version of AdaBoost for handling weak hypotheses with confidence rated predictions represents an important advance in the theory and practice of boosting. Its success results from a more efficient use of information in weak hypotheses during updating. Instead of simple binary voting a weak hypothesis is allowed to vote for or against a classification with a variable strength or confidence. The Pool Adjacent Violators (PAV) algorithm is a method for converting a score into a probability. We show how PAV may be applied to a weak hypothesis to yield a new weak hypothesis which is in a sense an ideal confidence rated prediction and that this leads to an optimal updating for AdaBoost. The result is a new algorithm which we term PAV-AdaBoost. We give several examples illustrating problems for which this new algorithm provides advantages in performance.