Algorithmic learning theory : 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001 : proceedings

Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings
Author: Naoki Abe, Roni Khardon, Thomas Zeugmann
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-42875-6
DOI: 10.1007/3-540-45583-3

Table of Contents:

• Editors’ Introduction
• The Discovery Science Project in Japan
• Queries Revisited
• Robot Baby 2001
• Discovering Mechanisms: A Computational Philosophy of Science Perspective
• Inventing Discovery Tools: Combining Information Visualization with Data Mining
• On Learning Correlated Boolean Functions Using Statistical Queries (Extended Abstract)
• A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm
• Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard
• Learning of Boolean Functions Using Support Vector Machines
• A Random Sampling Technique for Training Support Vector Machines
• Learning Coherent Concepts
• Learning Intermediate Concepts
• Real-Valued Multiple-Instance Learning with Queries
• Loss Functions, Complexities, and the Legendre Transformation
• Non-linear Inequalities between Predictive and Kolmogorov Complexities
• Learning by Switching Type of Information
• Learning How to Separate
• Learning Languages in a Union
• On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes

Includes bibliographical references and index