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

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ISBN-10: 3540407200

ISBN-13: 9783540407201

Edition: 2003

Authors: Bernhard Sch�lkopf, Manfred K. Warmuth

List price: $169.00
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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.
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Book details

List price: $169.00
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 Sch�lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in T�bingen, 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