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Bibliografische Details

Titel: Intelligent Data Engineering and Automated Learning - Ideal 2000: Data Mining, Financial Engineering, and Intelligent Agents : Second International Co . ngs (Lecture Notes in Computer Science, 1983)
Verlag: Springer-Verlag Telos
Erscheinungsjahr: 2000
Sprache: Englisch
ISBN-10: 3540414509
ISBN-13: 9783540414506
Einband: Softcover
Zustand: Good

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Inhaltsangabe

This book constitutes the refereed proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000, held in Shatin, N.T. Hong Kong, China in December 2000. The 81 revised papers presented were carefully reviewed and selected from numerous submissions. The book is divided in topical sections on data mining and automated learning, financial engineering, intelligent agents, Internet applications, multimedia processing, and genetic programming.

Rese�a del editor

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.

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