Algorithmic Learning Theory 11th International Conference, ALT 2000 Sydney, Australia, December 11-13, 2000 Proceedings

Bibliographic Details
Other Authors: Arimura, Hiroki (Editor), Jain, Sanjay (Editor), Sharma, Arun (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2000, 2000
Edition:1st ed. 2000
Series:Lecture Notes in Artificial Intelligence
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • INVITED LECTURES
  • Extracting Information from the Web for Concept Learning and Collaborative Filtering
  • The Divide-and-Conquer Manifesto
  • Sequential Sampling Techniques for Algorithmic Learning Theory
  • REGULAR CONTRIBUTIONS
  • 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
  • The Last-Step Minimax Algorithm
  • Rough Sets and Ordinal Classification
  • A note on the generalization performance of kernel classifiers with margin
  • On the Noise Model of Support Vector Machines Regression
  • Computationally Efficient Transductive Machines