 |
|
|
ADVANCES IN NEURAL NETWORK RESEARCH: IJCNN 2003
| |
| | |
|
|
To order this title, and for more information, click here
By
D.C. Wunsch II, University of Missouri-Rolla, Department of Electrical Computer Engineering, MO 65409, USA
M. Hasselmo, Boston University, Department of Psychology, MA 02215, USA
K. Venayagamoorthy, University of Missouri-Rolla, Department of Electrical Computer Engineering, MO 65409, USA
D. Wang, The Ohio State University, Department of Computer and Information Science, USA
Preface & Foreword
The Best of the Best
The top-reviewing papers from the 2003 International Joint Conference on Neural Networks
(IJCNN) have been expanded and assembled here in book format. In odd-numbered years,
IJCNN is led by the INNS, and in even-numbered years, by the IEEE. This year, we decided to
offer this select group of IJCNN authors a chance to expand their papers. The chapters of this
book appeared as a special issue of the Neural Networks journal in July 2003.
IJCNN is the flagship conference of the INNS, as well as the IEEE Neural Networks Society. It
has arguably been the preeminent conference in the field, even as neural network conferences
have proliferated and specialized. As the number of conferences has grown, its strongest
competition has migrated away from an emphasis on neural networks. IJCNN has embraced the
proliferation of spin-off and related fields (see the topic list, below), while maintaining a core
emphasis befitting its name. It has also succeeded in enforcing an emphasis on quality.
While being an inclusive conference, IJCNN has strict standards for acceptance, including
literature review, quality of English; and reproducibility, accuracy and meaningfulness of results.
All papers, even invited papers, were subject to a minimum of two reviews -- and many papers
received up to five. We rejected 15% of submitted papers, and only the top 10% of the remaining
papers are presented in this issue. These topics cover most of the major areas of research in
neural networks, including: self-organizing maps, reinforcement learning, support vector
machines, adaptive resonance theory, principal component analysis and independent component
analysis, as well as numerous engineering applications and detailed biological models of the
function of neural circuits.
IJCNN ?03 has, at this writing, surpassed expectations in every capacity. We got all our first
choices of plenary speakers: Kunihiko Fukushima, Earl Miller, Terrence Sejnowski, Vladimir
Vapnik, and Christoph von der Malsburg; an extraordinary slate of tutorial presenters, and 730
submitted articles -- 33% over projections. Papers are presented in 20-minute format in four
parallel sessions, planned to be as topically orthogonal as possible. Poster presentations are given
their own generous time slot as well.
If you haven't been to IJCNN lately, you don't know what you are missing. For more
information, see  http://www.ijcnn.net or http://www.inns.org or http://www.ieee-nns.org. It has been our pleasure
to work on creating the program for IJCNN, as well as this book, for you.
Sincerely,
Donald C. Wunsch II, University of Missouri-Rolla
General Chair, IJCNN '03
Mike Hasselmo, Boston University
Program Chair, IJCNN '03
DeLiang Wang, Ohio State University
Program Co-Chair, IJCNN '03
Ganesh Kumar Venayagamoorthy, University of Missouri-Rolla
Program Co-Chair, IJCNN '03
2003 International Joint
Conference on Neural Networks
TOPIC LIST
A. PERCEPTUAL AND MOTOR
FUNCTION
Vision and image processing
Pattern recognition
• Face recognition
• Handwriting recognition
• Other pattern recognition
Auditory and speech
processing
• Audition
Speech recognition
• Speech production
• Other perceptual systems
• Motor control and response
B. COGNITIVE FUNCTION
Cognitive information processing
Learning and memory
Spatial Navigation
Conditioning, Reward and Behavior
Mental disorders
Attention and Consciousness
Language
Emotion and Motivation
C. COMPUTATIONAL NEUROSCIENCE
Models of neurons and local circuits
Systems neurobiology and neural modeling
Spiking neurons
D. INFORMATICS
Neuroinformatics
Bioinformatics
Artificial immune systems
Data mining
E. HARDWARE
Neuromorphic hardware and implementations
Embedded neural networks
F. REINFORCEMENT LEARNING AND CONTROL
Reinforcement learning
Approximate/Adaptive dynamic programming
Control
Reconfigurable systems
Robotics
Fuzzy neural systems
Optimization
G. DYNAMICS
Neurodynamics
Recurrent networks
Chaos and learning theory
H. THEORY
Mathematics of Neural
Systems
Support vector machines
Extended Kalman filters
Mixture models, EM algorithms and ensemble learning
Radial basis functions
Self-organizing maps
Adaptive resonance theory
Principal component analysis and independent component analysis
Probabilistic and information-theoretic methods
Neural Networks and Evolutionary Computation
I. APPLICATIONS
Signal Processing
Telecommunications
Applications
Time Series Analysis
Biomedical Applications
Financial Engineering
Biomimetic applications
Computer security applications
Power system applications
Aeroinformatics
Diagnostics and Quality Control
Other applications
|
|
| |
| | |
|
|
| |
