- Machine Learning, 37(3):277296, 1999. Large Margin Classification
- Unsupervised Improvement of Visual Detectors using Co-Training School of CS and Eng.
- Machine Learning, 37(3):277-296, 1999. Large Margin Classification
- The Annals of Statistics 2004, Vol. 32, No. 4, 16981722
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- Machine Learning: Proceedings of the Thirteenth International Conference, 1996. Experiments with a New Boosting Algorithm
- Appearing in proceedings, 36th Annual Symposium on Foundations of Computer Science, November, 1995. Efficient Algorithms for Learning to Play Repeated Games
- Journal of the Association for Computing Machinery, 44(3):427485, 1997. How to Use Expert Advice
- The Annals of Statistics, 26(5):16511686, 1998. Boosting the Margin
- Journal of Japanese Society for ArtificialIntelligence,5 67800 57 September, 1999.
- Games and Economic Behavior, 29:79103, 1999. Adaptive game playing using multiplicative weights
- Journal of Japanese Society for Artificial Intelligence,14(5):771-780, September, 1999. (In Japanese, translation by Naoki Abe.)
- Proceedings of the TwentyNinth Annual ACM Symposium on the Theory of Computing, 1997. Using and combining predictors that specialize
- Learning under persistent drift Yoav Freund 1 and Yishay mansour 2?
- COLLECTIONS OF CLASSIFIERS TUNED FOR CELL FINDING WITH AN APPLICATION TO BUILDING DIGITAL CELL ATLASES OF DROSOPHILA EMBRYOS
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- Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, 1997. Using and combining predictors that specialize
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- Games and Economic Behavior, 29:79-103, 1999. Adaptive game playing using multiplicative weights
- Technical Report CS2007-0890 Department of Computer Science and Engineering University of California, San Diego
- An Online Learning-based Framework for Tracking Kamalika Chaudhuri
- Appearing in proceedings, 36th Annual Symposium on Foundations of Computer Science, November, 1995. Efficient Algorithms for Learning to Play Repeated Games
- The Annals of Statistics, 26(3):824-832, 1998. Discussion of the paper "Arcing Classifiers" by Leo Breiman
- Machine Learning: Proceedings of the Thirteenth International Conference, 1996. Experiments with a New Boosting Algorithm
- Continuous drifting games Yoav Freund
- Self bounding learning algorithms Yoav Freund
- Learning to model sequences generated by switching distributions Yoav Freund
- , , 1--38 () fl Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Predicting a binary sequence almost as well as the optimal biased coin
- Discussion of the Paper ``Additive Logistic Regression: A Statistical View of Boosting'' by
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- An introduction to boosting based classification Yoav Freund
- The nonstochastic multiarmed bandit problem Institute for Theoretical Computer Science
- Continuous drifting games Yoav Freund
- Submitted for journal publication. (An extended abstract appeared at ICML'98.)
- Efficient Learning of Typical Finite Automata from Random Walks Yoav Freund
- An adaptive version of the boost by majority algorithm Yoav Freund
- , , 1--42 () fl Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Estimating a mixture of two product distributions Yoav Freund
- A decisiontheoretic generalization of online learning and an application to boosting \Lambda
- COORDINATE-FREE CALIBRATION OF AN ACOUSTICALLY DRIVEN CAMERA POINTING SYSTEM
- An adaptive version of the boost by majority algorithm Yoav Freund
- A Parameter-free Hedging Algorithm Kamalika Chaudhuri
- PREDICTING PERFORMANCE AND QUANTIFYING CORPORATE GOVERNANCE RISK FOR LATIN AMERICAN ADRS AND BANKS
