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ORCID12th COLT 1999: Santa Cruz, CA, USA
- Shai Ben-David, Philip M. Long:
Proceedings of the Twelfth Annual Conference on Computational Learning Theory, COLT 1999, Santa Cruz, CA, USA, July 7-9, 1999. ACM 1999, ISBN 1-58113-167-4 - Claudio Gentile, Nick Littlestone:
The Robustness of the p-Norm Algorithms. 1-11 - Nicolò Cesa-Bianchi, Gábor Lugosi:
Minimax Regret Under log Loss for General Classes of Experts. 12-18 - Tsachy Weissman, Neri Merhav:
On Prediction of Individual Sequences Relative to a Set of Experts in the Presence of Noise. 19-28 - Geoffrey J. Gordon:
Regret Bounds for Prediction Problems. 29-40 - Robert H. Sloan, György Turán:
On Theory Revision with Queries. 41-52 - Yoav Freund, Yishay Mansour:
Estimating a Mixture of Two Product Distributions. 53-62 - Stephen Kwek:
An Apprentice Learning Model (extended abstract). 63-74 - Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon:
Uniform-Distribution Attribute Noise Learnability. 75-80 - Nader H. Bshouty, David K. Wilson:
On Learning in the Presence of Unspecified Attribute Values. 81-87 - Paul W. Goldberg
:
Learning Fixed-Dimension Linear Thresholds from Fragmented Data. 88-99 - David B. Shmoys:
Approximation Algorithms for Clustering Problems. 100-102 - Yoav Freund:
An Adaptive Version of the Boost by Majority Algorithm. 102-113 - Robert E. Schapire:
Drifting Games. 114-124 - John D. Lafferty:
Additive Models, Boosting, and Inference for Generalized Divergences. 125-133 - Jyrki Kivinen, Manfred K. Warmuth:
Boosting as Entropy Projection. 134-144 - Venkatesan Guruswami, Amit Sahai:
Multiclass Learning, Boosting, and Error-Correcting Codes. 145-155 - Tong Zhang:
Theoretical Analysis of a Class of Randomized Regularization Methods. 156-163 - David A. McAllester:
PAC-Bayesian Model Averaging. 164-170 - Peter Grünwald:
Viewing all Models as "Probabilistic". 171-182 - Yishay Mansour:
Reinforcement Learning and Mistake Bounded Algorithms. 183-192 - Vladislav Tadic:
Convergence Analysis of Temporal-Difference Learning Algorithms with Linear Function Approximation. 193-202 - Avrim Blum, Adam Kalai, John Langford:
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. 203-208 - John Langford, Avrim Blum:
Microchoice Bounds and Self Bounding Learning Algorithms. 209-214 - Atsuyoshi Nakamura:
Learning Specialist Decision Lists. 215-225 - Yuri Kalnishkan:
Linear Relations between Square-Loss and Kolmogorov Complexity. 226-232 - Chamy Allenberg:
Individual Sequence Prediction - Upper Bounds and Application for Complexity. 233-242 - Sebastiaan Terwijn:
Extensional Set Learning (extended abstract). 243-248 - Sanjay Jain, Arun Sharma:
On a Generalized Notion of Mistake Bounds. 249-256 - Efim B. Kinber, Christophe Papazian, Carl H. Smith, Rolf Wiehagen:
On the Intrinsic Complexity of Learning Recursive Functions. 257-266 - Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson:
Covering Numbers for Support Vector Machines. 267-277 - John Shawe-Taylor, Nello Cristianini:
Further Results on the Margin Distribution. 278-285 - Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon:
More Efficient PAC-Learning of DNF with Membership Queries Under the Uniform Distribution. 286-295 - Rocco A. Servedio:
On PAC Learning Using Winnow, Perceptron, and a Perceptron-like Algorithm. 296-307 - David Gamarnik:
Extension of the PAC Framework to Finite and Countable Markov Chains. 308-317 - Elias Abboud, Nader Agha, Nader H. Bshouty, Nizar Radwan, Fathi Saleh:
Learning Threshold Functions with Small Weights Using Membership Queries. 318-322 - Thomas R. Amoth, Paul Cull, Prasad Tadepalli:
Exact Learning of Unordered Tree Patterns from Queries. 323-332
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