Algorithmic Learning Theory

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.

Includes supplementary material: sn.pub/extras