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
Includes bibliographical references and index
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