Algorithmic Learning Theory

Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 ProceedingsAuthor: Hiroki Arimura, Sanjay Jain, Arun Sharma Published by Springer Berlin Heidelberg ISBN: 978-3-540-41237-3 DOI: 10.1007/3-540-40992-0Table 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