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