Algorithmic learning theory

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