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