- Joint SIGDAT Conference on Empirical Methodsinds 284 Language Processing and Very Large Corpora, 1999.
- Machine Learning, 14, 83-113 (1994) 1994 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands,
- Training Algorithms for Linear Text Classifiers David D. Lewis Robert E. Schapire
- Uncertainty in Artificial Intelligence: Proceedings ofthe Eighteenth Conference, 2002.
- Journal of Artificial Intelligence Research 10 (1999) 243270 Submitted 10/98; published 5/99 Learning to Order Things
- The Convergence Rate of AdaBoost Robert E. Schapire
- Adding Dense, Weighted Connections to WORDNET Jordan Boyd-Graber and Christiane Fellbaum and Daniel Osherson and Robert Schapire
- Proceedings of the TwentyFirst International Conference on Machine Learning, pages 655662, 2004. A Maximum Entropy Approach to Species Distribution Modeling
- Proceedings of Algorithmic Learning Theory, 1999. Theoretical Views of Boosting and Applications
- A decisiontheoretic generalization of online learning and an application to boosting \Lambda
- Advances in Neural Information Processing Systems 14, 2002 A Generalization of Principal Component
- Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Third Conference, 2007. Imitation Learning with a Value-Based Prior
- Journal of Machine Learning Research 4 (2003) 933969 Submitted 12/01; Revised 11/02; Published 11/03 An Efficient Boosting Algorithm for Combining Preferences
- Combining active and semi-supervised learning for spoken language understanding
- Proceedings of the Twenty-First International Conference on Machine Learning, pages 655-662, 2004. A Maximum Entropy Approach to Species Distribution Modeling
- BIOINFORMATICS ORIGINAL PAPER Vol. 25 no. 10 2009, pages 13071313
- Advances in Neural Information Processing Systems 20, 2008. A Game-Theoretic Approach to Apprenticeship
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- Machine Learning: Proceedings of the Nineteenth International Conference, 2002. Incorporating Prior Knowledge into Boosting
- Combining Spatial and Telemetric Features for Learning Animal Movement Models
- Machine Learning, 43, 265291, 2001. Drifting Games
- Advances in Neural Information Processing Systems 16, 2004. On the Dynamics of Boosting #
- Boosting for Document Routing Raj D. Iyer \Lambda
- Gambling in a rigged casino: The adversarial multiarmed bandit problem \Lambda
- The Annals of Statistics, 32(1), February, 2004. A Discussion of
- Agent Mediated Electronic Commerce IV: Designing Mechanismsandnisms 3 Springer Verlag, 2002.
- Appearing in Proceedings of the 19th Annual Conference on Learning Theory, 2006. Maximum Entropy Distribution Estimation with
- In Gerhard Lakemeyer and Bernhard Nebel, editors, Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann, 2002.
- Machine Learning 27(1):5168, 1997. Predicting nearly as well as
- Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996. Game Theory, Online Prediction and Boosting
- Mach Learn (2008) 71: 219242 DOI 10.1007/s10994-008-5056-8
- A Theory of Multiclass Boosting Indraneel Mukherjee Robert E. Schapire
- Journal of Machine Learning Research 1 (2000) 113-141 Submitted 5/00; Published 12/00 Reducing Multiclass to Binary
- Machine Learning, 37(3):297336, 1999. Improved Boosting Algorithms
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- Journal of Arti cial Intelligence Research 19 (2003) 209-242 Submitted 12/02; published 9/03 Decision-Theoretic Bidding Based on Learned Density
- Contextual Bandits with Linear Payoff Functions Wei Chu Lihong Li Lev Reyzin Robert E. Schapire
- The Annals of Statistics, in press. Discussion of the Paper ``Additive Logistic
- Nonlinear Estimation and Classification, Springer, 2003. The Boosting Approach to Machine Learning
- 174 IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 13, NO. 2, MARCH 2005 Boosting With Prior Knowledge for Call Classification
- Journal of Machine Learning Research 8 (2007) 1217-1260 Submitted 4/06; Revised 4/07; Published 6/07 Maximum Entropy Density Estimation with Generalized
- AT&T HELP DESK Giuseppe Di Fabbrizio, Dawn Dutton, Narendra Gupta, Barbara Hollister, Mazin Rahim, Giuseppe
- To appear in Advances in Neural Information Processing Systems 18, 2006. Correcting sample selection bias in maximum
- Contextual Bandit Algorithms with Supervised Learning Guarantees Alina Beygelzimer John Langford Lihong Li
- Speed and Sparsity of Regularized Boosting Yongxin Taylor Xi Zhen James Xiang Peter J. Ramadge
- Machine Learning: Proceedings of the Fourteenth International Conference, 1997. Using output codes to boost multiclass learning problems
- Machine Learning, 48(1/2/3), 2002. Logistic Regression, AdaBoost and Bregman Distances
- Computational Learning Theory: Fourth EuropeanConference, 66300 3 pages 110, 1999.
- Journal of Computer and System Sciences, 52(2):201213, April, 1996. Learning Sparse Multivariate Polynomials over a Field with
- Journal of Machine Learning Research 9 (2008) 171-174 Published 2/08 Response to Mease and Wyner, Evidence Contrary to the Statistical View
- information and computation 138, 23 48 (1997) Efficient Learning of Typical Finite
- Supplement: A Theory of Multiclass Boosting Indraneel Mukherjee Robert E. Schapire
- A Reduction from Apprenticeship Learning to Classification
- Non-Stochastic Bandit Slate Problems Satyen Kale
- A Contextual-Bandit Approach to Personalized News Article Recommendation
- From Optimization to Regret Minimization and Back Again Ioannis Avramopoulos
- How Boosting the Margin Can Also Boost Classifier Complexity Lev Reyzin lev.reyzin@yale.edu
- Algorithms for Portfolio Management based on the Newton Method Amit Agarwal aagarwal@cs.princeton.edu
- Vol. 22 no. 7 2006, pages 830836 doi:10.1093/bioinformatics/btk048BIOINFORMATICS ORIGINAL PAPER
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- journalsubmission.64300 version appeared in Proceedings of the Thirteenth Annual Conference
- Boosting and Rocchio Applied to Text Filtering Robert E. Schapire, Yoram Singer, Amit Singhal
- 17th Annual Conference on Computational Learning Theory, 2004. Boosting Based on a Smooth Margin #
- MachineLearning,5, 197-227(1990) 1990KluwerAcademicPublishers,Boston. Manufacturedin The Netherlands.
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- THE NONSTOCHASTIC MULTIARMED BANDIT PROBLEM # PETER AUER + , NICOL
- Proceedings of the Sixteenth InternationalJoint Conference on Artificial Intelligence, 1999.
- FilterBoost: Regression and Classification on Large Joseph K. Bradley
- Theoretical Computer Science 411 (2010) 26702683 Contents lists available at ScienceDirect
- International Conference on Accoustics, Speech and Signal Processing, 2002. COMBINING PRIOR KNOWLEDGE AND BOOSTING FOR CALL CLASSIFICATION IN
- BOOSTEXTER FOR TEXT CATEGORIZATION IN SPOKEN LANGUAGE DIALOGUE Marie Rochery, Robert Schapire, Mazin Rahim, Narendra Gupta
- COMPRESSIVE SENSING MEETS GAME THEORY Sina Jafarpour, Robert E. Schapire
- A Game Theoretic Approach to Expander-based Compressive Sensing
- The Rate of Convergence of AdaBoost Indraneel Mukherjee
