Algorithmic learning theory

About this book

Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 Proceedings Author: Hiroki Arimura, Sanjay Jain, Arun Sharma Published by Springer Berlin Heidelberg ISBN: 978-3-540-41237-3 DOI: 10.1007/3-540-40992-0 Table of Contents: Extracting Information from the Web for Concept Learning and Collaborative Filtering The Divide-and-Conquer Manifesto Sequential Sampling Techniques for Algorithmic Learning Theory Towards an Algorithmic Statistics Minimum Message Length Grouping of Ordered Data Learning From Positive and Unlabeled Examples Learning Erasing Pattern Languages with Queries Learning Recursive Concepts with Anomalies Identification of Function Distinguishable Languages A Probabilistic Identification Result A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System Hypotheses Finding via Residue Hypotheses with the Resolution Principle Conceptual Classifications Guided by a Concept Hierarchy Learning Taxonomic Relation by Case-based Reasoning Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees Self-duality of Bounded Monotone Boolean Functions and Related Problems Sharper Bounds for the Hardness of Prototype and Feature Selection On the Hardness of Learning Acyclic Conjunctive Queries Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm On Approximate Learning by Multi-layered Feedforward Circuits