Details

Autor(en) / Beteiligte

• ALT 2000
• ALT 2000
• Arimura, Hiroki[Herausgeber]
• Jain, Sanjay[Herausgeber]
• Sharma, Arun[Herausgeber]

Titel

Algorithmic Learning Theory : 11th International Conference, ALT 2000 Sydney, Australia, December 11-13, 2000 Proceedings [electronic resource]

Auflage

1st ed. 2000

Ort / Verlag

Berlin, Heidelberg : Springer Berlin Heidelberg

Erscheinungsjahr

2000

Link zum Volltext

• SpringerLink Books - AutoHoldings

Beschreibungen/Notizen

• Bibliographic Level Mode of Issuance: Monograph
• Includes bibliographical references at the end of each chapters and index.
• 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.
• English