Skip to main content
SpringerLink
Log in
Book cover
  • Conference proceedings
  • © 2000

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

Editors:

  1. Kwong Sak Leung

    1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Lai-Wan Chan

    1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Helen Meng

    1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

Sections

Buy it now

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

This is a preview of subscription content, log in via an institution to check for access.

Table of contents (82 papers)

  1. Front Matter

    Pages I-XVI
    PDF

  2. Data Mining and Automated Learning

    2. Classification

      1. Quantization of Continuous Input Variables for Binary Classification
        • MichaÅ‚ Skubacz, Jaakko Hollmén
        Pages 42-47
      2. Information-Based Classification by Aggregating Emerging Patterns
        • Xiuzhen Zhang, Guozhu Dong, 1Kotagiri Ramamohanarao
        Pages 48-53
      3. Boosting the Margin Distribution
        • Huma Lodhi, Grigoris Karakoulas, John Shawe-Taylor
        Pages 54-59
      4. Detecting a Compact Decision Tree Based on an Appropriate Abstraction
        • Yoshimitsu Kudoh, 1Makoto Haraguchi
        Pages 60-70
      5. A New Algorithm to Select Learning Examples from Learning Data
        • B. Chebel-Morello, E. Lereno, B. P. Baptiste
        Pages 71-78
      6. Data Ranking Based on Spatial Partitioning
        • Gongde Guo, Hui Wang, David Bell
        Pages 78-84

    3. Association Rules and Fuzzy Rules

      1. Logical Decision Rules: Teaching C4.5 to Speak Prolog
        • Kamran Karimi, Howard J. Hamilton
        Pages 85-90
      2. Visualisation of Temporal Interval Association Rules
        • Chris P. Rainsford, John F. Roddick
        Pages 91-96
      3. Lithofacies Characteristics Discovery from Well Log Data Using Association Rules
        • C. C. Fung, K. W. Law, K. W. Wong, P. Rajagopalan
        Pages 97-102
      5. A Data-Driven Fuzzy Approach to Robot Navigation Among Moving Obstacles
        • Mohannad Al-Khatib, Jean J. Saade
        Pages 109-115

    4. Learning Systems

      1. Best Harmony Learning
        • Lei Xu
        Pages 116-125
      2. Observational Learning with Modular Networks
        • Hyunjung Shin, Hyoungjoo Lee, Sungzoon Cho
        Pages 126-132
Back to top

About this book

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.
Back to top

Keywords

Back to top

Editors and Affiliations

  • Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Kwong Sak Leung, Lai-Wan Chan

  • Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

Back to top

Bibliographic Information

Back to top

Publish with us

Policies and ethics

Back to top
Access via your institution

Buy it now

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Search

Navigation