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 Sak Leung
  • Lai-Wan Chan
  • Helen Meng
Conference proceedings IDEAL 2000

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1983)

Table of contents

  1. Front Matter
    Pages I-XVI
  2. Data Mining and Automated Learning

    1. Clustering

    2. Classification

      1. Michał Skubacz, Jaakko Hollmén
        Pages 42-47
      2. Xiuzhen Zhang, Guozhu Dong, 1Kotagiri Ramamohanarao
        Pages 48-53
      3. Huma Lodhi, Grigoris Karakoulas, John Shawe-Taylor
        Pages 54-59
      4. Yoshimitsu Kudoh, 1Makoto Haraguchi
        Pages 60-70
      5. B. Chebel-Morello, E. Lereno, B. P. Baptiste
        Pages 71-78
      6. Gongde Guo, Hui Wang, David Bell
        Pages 78-84
    3. Association Rules and Fuzzy Rules

      1. Kamran Karimi, Howard J. Hamilton
        Pages 85-90
      2. Chris P. Rainsford, John F. Roddick
        Pages 91-96
      3. C. C. Fung, K. W. Law, K. W. Wong, P. Rajagopalan
        Pages 97-102
      4. Mohannad Al-Khatib, Jean J. Saade
        Pages 109-115
    4. Learning Systems

      1. Lei Xu
        Pages 116-125
      2. Hyunjung Shin, Hyoungjoo Lee, Sungzoon Cho
        Pages 126-132

About these proceedings


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.


Algorithmic Learning Computational Finance Data Engineering Financial Engineering Image Processing Intelligent Information Processing Internet Agents Knowledge Discovery agents data mining genetic programming learning multimedia

Editors and affiliations

  • Kwong Sak Leung
    • 1
  • Lai-Wan Chan
    • 1
  • Helen Meng
    • 2
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong
  2. 2.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatinHong Kong

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2000
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-41450-6
  • Online ISBN 978-3-540-44491-6
  • Series Print ISSN 0302-9743
  • About this book
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