Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings

Target Area: Computational Game Theory. - Tutorial: Learning Topics in Game-Theoretic Decision Making. - A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria. - Preference Elicitation and Query Learning. - Efficient Algorithms for Online Decision Problems. - Positive Definite Rational Kernels. - Bhattacharyya and Expected Likelihood Kernels. - Maximal Margin Classification for Metric Spaces. - Maximum Margin Algorithms with Boolean Kernels. - Knowledge-Based Nonlinear Kernel Classifiers. - Fast Kernels for Inexact String Matching. - On Graph Kernels: Hardness Results and Efficient Alternatives. - Kernels and Regularization on Graphs. - Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. - Poster Session 1. - Comparing Clusterings by the Variation of Information. - Multiplicative Updates for Large Margin Classifiers. - Simplified PAC-Bayesian Margin Bounds. - Sparse Kernel Partial Least Squares Regression. - Sparse Probability Regression by Label Partitioning. - Learning with Rigorous Support Vector Machines. - Robust Regression by Boosting the Median. - Boosting with Diverse Base Classifiers. - Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming. - Optimal Rates of Aggregation. - Distance-Based Classification with Lipschitz Functions. - Random Subclass Bounds. - PAC-MDL Bounds. - Universal Well-Calibrated Algorithm for On-Line Classification. - Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling. - Learning Algorithms for Enclosing Points in Bregmanian Spheres. - Internal Regret in On-Line Portfolio Selection. - Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem. - Smooth ? -Insensitive Regression by Loss Symmetrization. - On Finding Large Conjunctive Clusters. - Learning Arithmetic Circuits via Partial Derivatives. - Poster Session 2. - Using a Linear Fit to Determine Monotonicity Directions. - Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering. - Sequence Prediction Based on Monotone Complexity. - How Many Strings Are Easy to Predict? - Polynomial Certificates for Propositional Classes. - On-Line Learning with Imperfect Monitoring. - Exploiting Task Relatedness for Multiple Task Learning. - Approximate Equivalence of Markov Decision Processes. - An Information Theoretic Tradeoff between Complexity and Accuracy. - Learning Random Log-Depth Decision Trees under the Uniform Distribution. - Projective DNF Formulae and Their Revision. - Learning with Equivalence Constraints and the Relation to Multiclass Learning. - Target Area: Natural Language Processing. - Tutorial: Machine Learning Methods in Natural Language Processing. - Learning from Uncertain Data. - Learning and Parsing Stochastic Unification-Based Grammars. - Generality's Price. - On Learning to Coordinate. - Learning All Subfunctions of a Function. - When Is Small Beautiful? - Learning a Function of r Relevant Variables. - Subspace Detection: A Robust Statistics Formulation. - How Fast Is k-Means? - Universal Coding of Zipf Distributions. - An Open Problem Regarding the Convergence of Universal A Priori Probability. - Entropy Bounds for Restricted Convex Hulls. - Compressing to VC Dimension Many Points.