Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents | Second International Conference Shatin, N.T., Hong Kong, China, December 13-15, 2000. Proceedings

Kwong S. Leung (u. a.)

ISBN 10: 3540414509 / ISBN 13: 9783540414506

Published by Springer, 2000

About this Item

Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents | Second International Conference Shatin, N.T., Hong Kong, China, December 13-15, 2000. Proceedings | Kwong S. Leung (u. a.) | Taschenbuch | xvi | Englisch | 2000 | Springer | EAN 9783540414506 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

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Bibliographic Details

• Title: Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents | Second International Conference Shatin, N.T., Hong Kong, China, December 13-15, 2000. Proceedings
• Publisher: Springer
• Publication Date: 2000
• Language: English
• ISBN 10: 3540414509
• ISBN 13: 9783540414506
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 32D millimeters 235 millimeters width by 155 millimeters height by 32 millimeters depth
• Weight: 890 grams
• Seller catalogs:
  • Bücher

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Synopsis

X Table of Contents Table of Contents XI XII Table of Contents Table of Contents XIII XIV Table of Contents Table of Contents XV XVI Table of Contents K.S. Leung, L.-W. Chan, and H. Meng (Eds.): IDEAL 2000, LNCS 1983, pp. 3›8, 2000. Springer-Verlag Berlin Heidelberg 2000 4 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 5 6 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 7 0.6 1.5 0.4 1 0.2 0.5 0 0 10 100 1000 10000 10 100 1000 Mutual information (bits) Mutual information (bits) 8 J. Sinkkonen and S. Kaski 20 10 0 0.1 0.3 0.5 0.7 Mutual information (mbits) Analyses on the Generalised Lotto-Type Competitive Learning Andrew Luk St B&P Neural Investments Pty Limited, Australia Abstract, In generalised lotto-type competitive learning algorithm more than one winner exist. The winners are divided into a number of tiers (or divisions), with each tier being rewarded differently. All the losers are penalised (which can be equally or differently). In order to study the various properties of the generalised lotto-type competitive learning, a set of equations, which governs its operations, is formulated. This is then used to analyse the stability and other dynamic properties of the generalised lotto-type competitive learning.