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Intelligent Data Engineering and Automated Learning -- IDEAL 2010

11th International Conference, Paisley, UK, September 1-3, 2010, Proceedings

  • Conference proceedings
  • © 2010

Overview

Editors:
  1. Colin Fyfe

    1. School of Computing, University of the West of Scotland, Paisley, UK

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  2. Peter Tino

    1. University of Birmingham, Birmingham, UK

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  3. Darryl Charles

    1. University of Ulster, Coleraine, UK

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  4. Cesar Garcia-Osorio

    1. Universidad de Burgos, Burgos, Spain

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  5. Hujun Yin

    1. School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6283)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: IDEAL 2010.

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About this book

The IDEAL conference has become a unique, established and broad interdisciplinary forum for experts, researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. This volume contains the papers presented at the 11th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2010), which was held September 1–3, 2010 in the University of the West of Scotland, on its Paisley campus, 15 kilometres from the city of Glasgow, Scotland. All submissions were strictly pe- reviewed by the Programme Committee and only the papers judged with sufficient quality and novelty were accepted and included in the proceedings. The IDEAL conferences continue to evolve and this year’s conference was no exc- tion. The conference papers cover a wide variety of topics which can be classified by technique, aim or application. The techniques include evolutionary algorithms, artificial neural networks, association rules, probabilistic modelling, agent modelling, particle swarm optimization and kernel methods. The aims include regression, classification, clustering and generic data mining. The applications include biological information processing, text processing, physical systems control, video analysis and time series analysis.

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Table of contents (47 papers)

  1. Front Matter

    Download chapter PDF

  2. Large Scale Instance Selection by Means of a Parallel Algorithm

    • Aida de Haro-García, Juan Antonio Romero del Castillo, Nicolás García-Pedrajas
    Pages 1-12

  3. Typed Linear Chain Conditional Random Fields and Their Application to Intrusion Detection

    • Carsten Elfers, Mirko Horstmann, Karsten Sohr, Otthein Herzog
    Pages 13-20

  4. Generalized Derivative Based Kernelized Learning Vector Quantization

    • Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl
    Pages 21-28

  5. Cost Optimization of a Localized Irrigation System Using Genetic Algorithms

    • Mônica Sakuray Pais, Júlio César Ferreira, Marconi Batista Teixeira, Keiji Yamanaka, Gilberto Arantes Carrijo
    Pages 29-36

  6. Dimension Reduction for Regression with Bottleneck Neural Networks

    • Elina Parviainen
    Pages 37-44

  7. Analysing Satellite Image Time Series by Means of Pattern Mining

    • François Petitjean, Pierre Gançarski, Florent Masseglia, Germain Forestier
    Pages 45-52

  8. Sentences Generation by Frequent Parsing Patterns

    • Takashi Yanagisawa, Takao Miura, Isamu Shioya
    Pages 53-62

  9. Gallbladder Boundary Segmentation from Ultrasound Images Using Active Contour Model

    • Marcin Ciecholewski
    Pages 63-69

  10. On the Power of Topological Kernel in Microarray-Based Detection of Cancer

    • Vilen Jumutc, Pawel Zayakin
    Pages 70-77

  11. An Evolutionary Multi-objective Optimization of Market Structures Using PBIL

    • Xinyang Li, Andreas Krause
    Pages 78-85

  12. New Application of Graph Mining to Video Analysis

    • Hisashi Koga, Tsuji Tomokazu, Takanori Yokoyama, Toshinori Watanabe
    Pages 86-93

  13. Classification by Multiple Reducts-kNN with Confidence

    • Naohiro Ishii, Yuichi Morioka, Hiroaki Kimura, Yongguang Bao
    Pages 94-101

  14. Towards Automatic Classification of Wikipedia Content

    • Julian SzymaÅ„ski
    Pages 102-109

  15. Investigating the Behaviour of Radial Basis Function Networks in Regression and Classification of Geospatial Data

    • Andrea Guidali, Elisabetta Binaghi, Mauro Guglielmin, Marco Pascale
    Pages 110-117

  16. A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm

    • Clinton Cohagan, Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe
    Pages 118-125

  17. Simplified Self-Adapting Skip Lists

    • Jonathan J. Pittard, Alan L. Tharp
    Pages 126-136

  18. Multi-Agent Architecture with Support to Quality of Service and Quality of Control

    • Jose-Luis Poza-Luján, Juan-Luis Posadas-Yagüe, Jose-Enrique Simó-Ten
    Pages 137-144

  19. Robust 1-Norm Soft Margin Smooth Support Vector Machine

    • Li-Jen Chien, Yuh-Jye Lee, Zhi-Peng Kao, Chih-Cheng Chang
    Pages 145-152

  20. A Generalization of Independence in Naive Bayes Model

    • Yu Fujimoto, Noboru Murata
    Pages 153-161
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Other volumes

  1. Intelligent Data Engineering and Automated Learning – IDEAL 2010

Editors and Affiliations

  • School of Computing, University of the West of Scotland, Paisley, UK

    Colin Fyfe

  • University of Birmingham, Birmingham, UK

    Peter Tino

  • University of Ulster, Coleraine, UK

    Darryl Charles

  • Universidad de Burgos, Burgos, Spain

    Cesar Garcia-Osorio

  • School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK

    Hujun Yin

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