Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

封面
Springer Science & Business Media, 2013年3月14日 - 307 頁
Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.
 

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內容

Prologue e o e a s e e e s
6
Evolving Connectionist Systems for Unsupervised Learning
31
Evolving Connectionist Systems for Supervised Learning
57
Recurrent Evolving Systems Reinforcement Learning
91
Evolving NeuroFuzzy Inference Systems
99
Evolutionary Computation and Evolving Connectionist
125
of OffLine Learning Connectionist Systems
131
Evolving Connectionist Machines Framework Biological
143
Dynamic Modelling of Brain Functions and Cognitive
193
Modelling the Emergence of Acoustic Segments Phonemes
209
OnLine Adaptive Speech Recognition
229
Adaptive Speech Recognition Systems
240
Evolving Systems for Integrated MultiModal Information
257
Epilogue
273
Extended Glossary
291
Index
305

Applications
164

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