Algorithmic learning theory : 15th international conference, ALT 2004, Padova, Italy, October 2-5, 2004 : proceedings

Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings
Author: Shoham Ben-David, John Case, Akira Maruoka
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-23356-5
DOI: 10.1007/b100989

Table of Contents:

• String Pattern Discovery
• Applications of Regularized Least Squares to Classification Problems
• Probabilistic Inductive Logic Programming
• Hidden Markov Modelling Techniques for Haplotype Analysis
• Learning, Logic, and Probability: A Unified View
• Learning Languages from Positive Data and Negative Counterexamples
• Inductive Inference of Term Rewriting Systems from Positive Data
• On the Data Consumption Benefits of Accepting Increased Uncertainty
• Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space
• Learning r-of-k Functions by Boosting
• Boosting Based on Divide and Merge
• Learning Boolean Functions in AC
• Decision Trees: More Theoretical Justification for Practical Algorithms
• Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data
• Complexity of Pattern Classes and Lipschitz Property
• On Kernels, Margins, and Low-Dimensional Mappings
• Estimation of the Data Region Using Extreme-Value Distributions
• Maximum Entropy Principle in Non-ordered Setting
• Universal Convergence of Semimeasures on Individual Random Sequences
• A Criterion for the Existence of Predictive Complexity for Binary Games

Includes bibliographical references and index