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

Name: Learning Theory and Kernel Machines 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, August 2003 - Proceedings
Price: 109.99 USD
Availability: InStock
ISBN: 9783540407201

ISBN-10: 3540407200

ISBN-13: 9783540407201

Edition: 2003

Authors: Bernhard Schï¿½lkopf, Manfred K. Warmuth

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Description:

This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003.The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.

Book details

List price: $109.99
Copyright year: 2003
Publisher: Springer Berlin / Heidelberg
Publication date: 8/11/2003
Binding: Paperback
Pages: 754
Size: 6.10" wide x 9.25" long x 1.25" tall
Weight: 5.126
Language: English

Bernhard Schï¿½lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tï¿½bingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.



Target Area: Computational Game Theory


Tutorial: Learning Topics in Game-Theoretic Decision Making



Invited Talk


A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria



Contributed Talks


Preference Elicitation and Query Learning



Efficient Algorithms for Online Decision Problems



Kernel Machines


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



Statistical Learning Theory


Optimal Rates of Aggregation



Distance-Based Classification with Lipschitz Functions



Random Subclass Bounds



PAC-MDL Bounds



Online Learning


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



Other Approaches


Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem



Smooth e-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



Invited Talks


Learning from Uncertain Data



Learning and Parsing Stochastic Unification-Based Grammars



Inductive Inference Learning


Generality's Price: Inescapable Deficiencies in Machine-Learned Programs



On Learning to Coordinate: Random Bits Help, Insightful Normal Forms, and Competency Isomorphisms



Learning All Subfunctions of a Function



Open Problems


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



Author Index