Neural Information Processing. Models and Applications: 17th International Conference, ICONIP 2010, Sydney, Australia, November 21-25, 2010, Proceedings, Part II | SpringerLink
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Neural Information Processing. Models and Applications

17th International Conference, ICONIP 2010, Sydney, Australia, November 21-25, 2010, Proceedings, Part II

  • Conference proceedings
  • © 2010

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Editors:
  1. Kok Wai Wong

    1. School of Information Technology, Murdoch University, Murdoch, Australia

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  2. B. Sumudu U. Mendis

    1. The Australian National University, Canberra, Australia

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  3. Abdesselam Bouzerdoum

    1. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6444)

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

Included in the following conference series:

Conference proceedings info: ICONIP 2010.

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About this book

The two volume set LNCS 6443 and LNCS 6444 constitutes the proceedings of the 17th International Conference on Neural Information Processing, ICONIP 2010, held in Sydney, Australia, in November 2010. The 146 regular session papers presented were carefully reviewed and selected from 470 submissions. The papers of part I are organized in topical sections on neurodynamics, computational neuroscience and cognitive science, data and text processing, adaptive algorithms, bio-inspired algorithms, and hierarchical methods. The second volume is structured in topical sections on brain computer interface, kernel methods, computational advance in bioinformatics, self-organizing maps and their applications, machine learning applications to image analysis, and applications.

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Keywords

Table of contents (89 papers)

  1. Front Matter

    Download chapter PDF

  2. Brain Computer Interface

    1. Utilizing Fuzzy-SVM and a Subject Database to Reduce the Calibration Time of P300-Based BCI

      • Sercan Taha Ahi, Natsue Yoshimura, Hiroyuki Kambara, Yasuharu Koike
      Pages 1-8

    2. Importance Weighted Extreme Energy Ratio for EEG Classification

      • Wenting Tu, Shiliang Sun
      Pages 9-16

    3. Toward Automated Electrode Selection in the Electronic Depth Control Strategy for Multi-unit Recordings

      • Gert Van Dijck, Ahmad Jezzini, Stanislav Herwik, Sebastian Kisban, Karsten Seidl, Oliver Paul et al.
      Pages 17-25

    4. Tensor Based Simultaneous Feature Extraction and Sample Weighting for EEG Classification

      • Yoshikazu Washizawa, Hiroshi Higashi, Tomasz Rutkowski, Toshihisa Tanaka, Andrzej Cichocki
      Pages 26-33

    5. A Tongue-Machine Interface: Detection of Tongue Positions by Glossokinetic Potentials

      • Yunjun Nam, Qibin Zhao, Andrzej Cichocki, Seungjin Choi
      Pages 34-41

    6. Practical Surface EMG Pattern Classification by Using a Selective Desensitization Neural Network

      • Hiroshi Kawata, Fumihide Tanaka, Atsuo Suemitsu, Masahiko Morita
      Pages 42-49

    7. Reliability-Based Automatic Repeat reQuest with Error Potential-Based Error Correction for Improving P300 Speller Performance

      • Hiromu Takahashi, Tomohiro Yoshikawa, Takeshi Furuhashi
      Pages 50-57

    8. An Augmented-Reality Based Brain-Computer Interface for Robot Control

      • Alexander Lenhardt, Helge Ritter
      Pages 58-65

    9. Brain Computer Interfaces: A Recurrent Neural Network Approach

      • Gareth Oliver, Tom Gedeon
      Pages 66-73

    10. Research on Relationship between Saccade-Related EEG Signals and Selection of Electrode Position by Independent Component Analysis

      • Arao Funase, Motoaki Mouri, Andrzej Cichocki, Ichi Takumi
      Pages 74-81

  3. Kernel Methods

    1. Application of SVM-Based Filter Using LMS Learning Algorithm for Image Denoising

      • Tzu-Chao Lin, Chien-Ting Yeh, Mu-Kun Liu
      Pages 82-90

    2. Tuning N-gram String Kernel SVMs via Meta Learning

      • Nuwan Gunasekara, Shaoning Pang, Nikola Kasabov
      Pages 91-98

    3. Bilinear Formulated Multiple Kernel Learning for Multi-class Classification Problem

      • Takumi Kobayashi, Nobuyuki Otsu
      Pages 99-107

    4. Feature Extraction Using Support Vector Machines

      • Yasuyuki Tajiri, Ryosuke Yabuwaki, Takuya Kitamura, Shigeo Abe
      Pages 108-115

    5. Class Information Adapted Kernel for Support Vector Machine

      • Tasadduq Imam, Kevin Tickle
      Pages 116-123

    6. Gaze Pattern and Reading Comprehension

      • Tan Vo, B. Sumudu U. Mendis, Tom Gedeon
      Pages 124-131

    7. A Theoretical Framework for Multi-sphere Support Vector Data Description

      • Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
      Pages 132-142

    8. Fast Implementation of String-Kernel-Based Support Vector Classifiers by GPU Computing

      • Yongquan Shi, Tao Ban, Shanqing Guo, Qiuliang Xu, Youki Kadobayashi
      Pages 143-151

  4. Model Generation and Classification

    1. Classification of Imbalanced Data by Combining the Complementary Neural Network and SMOTE Algorithm

      • Piyasak Jeatrakul, Kok Wai Wong, Chun Che Fung
      Pages 152-159
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Other volumes

  1. Neural Information Processing. Theory and Algorithms

  2. Neural Information Processing. Models and Applications

Editors and Affiliations

  • School of Information Technology, Murdoch University, Murdoch, Australia

    Kok Wai Wong

  • The Australian National University, Canberra, Australia

    B. Sumudu U. Mendis

  • School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, Australia

    Abdesselam Bouzerdoum

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