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 / edited by Bernhard Schölkopf, Manfred K. Warmuth.

Format:

Book

Contributor:

Schölkopf, Bernhard, editor.

Warmuth, Manfred K., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2777.

Lecture Notes in Artificial Intelligence ; 2777

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Algorithms.

Logic, Symbolic and mathematical.

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Local Subjects:

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Physical Description:

1 online resource (XIV, 754 pages).

Edition:

First edition 2003.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.

System Details:

text file PDF

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-45167-9

9783540451679

Access Restriction:

Restricted for use by site license.