Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II | SpringerLink
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Advances in Neural Networks - ISNN 2006

Third International Symposium on Neural Networks, ISNN 2006, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II

  • Conference proceedings
  • © 2006

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Editors:
  1. Jun Wang

    1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China

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  2. Zhang Yi

    1. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China

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  3. Jacek M. Zurada

    1. Department of Electrical Engineering, University of Louisville, Louisville, U.S.A

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  4. Bao-Liang Lu

    1. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, Shanghai, China

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  5. Hujun Yin

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

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3972)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ISNN 2006.

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About this book

This book and its sister volumes constitute the Proceedings of the Third International Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China during May 28–31, 2006. After a successful ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN became a well-established series of conferences on neural computation in the region with growing popularity and improving quality. ISNN 2006 received 2472 submissions from authors in 43 countries and regions (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, UK, Ireland, Canada, USA, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews, 616 high-quality papers were selected for publication in the proceedings with the acceptance rate being less than 25%. The papers are organized in 27 cohesive sections covering all major topics of neural network research and development. In addition to the numerous contributed papers, ten distinguished scholars gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).

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Keywords

Table of contents (205 papers)

  1. Front Matter

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  2. Pattern Classification

    1. Design an Effective Pattern Classification Model

      • Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim
      Pages 1-7

    2. Classifying Unbalanced Pattern Groups by Training Neural Network

      • Bo-Yu Li, Jing Peng, Yan-Qiu Chen, Ya-Qiu Jin
      Pages 8-13

    3. A Modified Constructive Fuzzy Neural Networks for Classification of Large-Scale and Complicated Data

      • Lunwen Wang, Yanhua Wu, Ying Tan, Ling Zhang
      Pages 14-19

    4. A Hierarchical FloatBoost and MLP Classifier for Mobile Phone Embedded Eye Location System

      • Dan Chen, Xusheng Tang, Zongying Ou, Ning Xi
      Pages 20-25

    5. Iris Recognition Using LVQ Neural Network

      • Seongwon Cho, Jaemin Kim
      Pages 26-33

    6. Minimax Probability Machine for Iris Recognition

      • Yong Wang, Jiu-qiang Han
      Pages 34-39

    7. Detecting Facial Features by Heteroassociative Memory Neural Network Utilizing Facial Statistics

      • Kyeong-Seop Kim, Tae-Ho Yoon, Seung-Won Shin
      Pages 40-48

    8. Recognizing Partially Damaged Facial Images by Subspace Auto-associative Memories

      • Xiaorong Pu, Zhang Yi, Yue Wu
      Pages 49-54

    9. A Facial Expression Classification Algorithm Based on Principle Component Analysis

      • Qingzhang Chen, Weiyi Zhang, Xiaoying Chen, Jianghong Han
      Pages 55-62

    10. Automatic Facial Expression Recognition

      • Huchuan Lu, Pei Wu, Hui Lin, Deli Yang
      Pages 63-68

    11. Facial Expression Recognition Using Active Appearance Model

      • Taehwa Hong, Yang-Bok Lee, Yong-Guk Kim, Hagbae Kim
      Pages 69-76

    12. Facial Expression Recognition Based on BoostingTree

      • Ning Sun, Wenming Zheng, Changyin Sun, Cairong Zou, Li Zhao
      Pages 77-84

    13. KDA Plus KPCA for Face Recognition

      • Wenming Zheng
      Pages 85-92

    14. Face Recognition Using a Neural Network Simulating Olfactory Systems

      • Guang Li, Jin Zhang, You Wang, Walter J. Freeman
      Pages 93-97

    15. Face Recognition Using Neural Networks and Pattern Averaging

      • Adnan Khashman
      Pages 98-103

    16. Semi-supervised Support Vector Learning for Face Recognition

      • Ke Lu, Xiaofei He, Jidong Zhao
      Pages 104-109

    17. Parts-Based Holistic Face Recognition with RBF Neural Networks

      • Wei Zhou, Xiaorong Pu, Ziming Zheng
      Pages 110-115

    18. Combining Classifiers for Robust Face Detection

      • Lin-Lin Huang, Akinobu Shimizu
      Pages 116-121

    19. Face Detection Method Based on Kernel Independent Component Analysis and Boosting Chain Algorithm

      • Yan Wu, Yin-Fang Zhuang
      Pages 122-127
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Other volumes

  1. Advances in Neural Networks - ISNN 2006

  2. Advances in Neural Networks - ISNN 2006

  3. Advances in Neural Networks - ISNN 2006

Editors and Affiliations

  • Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Jun Wang

  • Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China

    Zhang Yi

  • Department of Electrical Engineering, University of Louisville, Louisville, U.S.A

    Jacek M. Zurada

  • Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, Shanghai, China

    Bao-Liang Lu

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

    Hujun Yin

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