COLT 1999

36 papers

Additive Models, Boosting, and Inference for Generalized Divergences John D. Lafferty

An Adaptive Version of the Boost by Majority Algorithm Yoav Freund

An Apprentice Learning Model (extended Abstract) Stephen Kwek

Approximation Algorithms for Clustering Problems David B. Shmoys

Beating the Hold-Out: Bounds for K-Fold and Progressive Cross-Validation Avrim Blum, Adam Kalai, John Langford

Boosting as Entropy Projection Jyrki Kivinen, Manfred K. Warmuth

Convergence Analysis of Temporal-Difference Learning Algorithms with Linear Function Approximation Vladislav Tadic

Covering Numbers for Support Vector Machines Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson

Drifting Games Robert E. Schapire

Estimating a Mixture of Two Product Distributions Yoav Freund, Yishay Mansour

Exact Learning of Unordered Tree Patterns from Queries Thomas R. Amoth, Paul Cull, Prasad Tadepalli

Extension of the PAC Framework to Finite and Countable Markov Chains David Gamarnik

Extensional Set Learning (extended Abstract) Sebastiaan Terwijn

Further Results on the Margin Distribution John Shawe-Taylor, Nello Cristianini

Individual Sequence Prediction - Upper Bounds and Application for Complexity Chamy Allenberg

Learning Fixed-Dimension Linear Thresholds from Fragmented Data Paul W. Goldberg

Learning Specialist Decision Lists Atsuyoshi Nakamura

Learning Threshold Functions with Small Weights Using Membership Queries Elias Abboud, Nader Agha, Nader H. Bshouty, Nizar Radwan, Fathi Saleh

Linear Relations Between Square-Loss and Kolmogorov Complexity Yuri Kalnishkan

Microchoice Bounds and Self Bounding Learning Algorithms John Langford, Avrim Blum

Minimax Regret Under Log Loss for General Classes of Experts Nicolò Cesa-Bianchi, Gábor Lugosi

More Efficient PAC-Learning of DNF with Membership Queries Under the Uniform Distribution Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon

Multiclass Learning, Boosting, and Error-Correcting Codes Venkatesan Guruswami, Amit Sahai

On a Generalized Notion of Mistake Bounds Sanjay Jain, Arun Sharma

On Learning in the Presence of Unspecified Attribute Values Nader H. Bshouty, David K. Wilson

On PAC Learning Using Winnow, Perceptron, and a Perceptron-like Algorithm Rocco A. Servedio

On Prediction of Individual Sequences Relative to a Set of Experts in the Presence of Noise Tsachy Weissman, Neri Merhav

On the Intrinsic Complexity of Learning Recursive Functions Efim B. Kinber, Christophe Papazian, Carl H. Smith, Rolf Wiehagen

On Theory Revision with Queries Robert H. Sloan, György Turán

PAC-Bayesian Model Averaging David A. McAllester

Regret Bounds for Prediction Problems Geoffrey J. Gordon

Reinforcement Learning and Mistake Bounded Algorithms Yishay Mansour

The Robustness of the P-Norm Algorithms Claudio Gentile, Nick Littlestone

Theoretical Analysis of a Class of Randomized Regularization Methods Tong Zhang

Uniform-Distribution Attribute Noise Learnability Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon

Viewing All Models as "Probabilistic" Peter Grünwald