Advances in Neural Networks - ISNN 2006

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

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).

• Hardware
• algorithm
• algorithms
• bioinformatics
• classification
• communication
• computer
• computer vision
• image processing
• learning
• modeling
• neuroinformatics
• optimization
• robot
• system modeling
• algorithm analysis and problem complexity

• 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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