Evolving Connectionist Systems: A Knowle
β Nikola Kasabov
π Library
π
2007
π English
β¦ LIBER β¦
π
Evolving Connectionist Systems: The Knowledge Engineering Approach
β Scribed by Nikola Kasabov (auth.)
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Leaves
- 451
- Edition
- 2
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
Evolving Connectionist Systems is aimed at all those interested in developing and using intelligent computational models and systems to solve challenging real world problems in computer science, engineering, bioinformatics and neuroinformatics. The book challenges scientists and practitioners with open questions about future creation of new information models inspired by Nature.
This second edition includes new methods for adaptive, knowledge-based learning, such as online incremental feature selection, spiking neural networks, transductive neuro-fuzzy inference, adaptive data and model integration, cellular automata and artificial life systems, particle swarm optimisation, ensembles of evolving systems, and quantum inspired neural networks.
New applications to gene and protein interaction modelling, brain data analysis and brain model creation, computational neuro-genetic modelling, adaptive speech, image and multimodal recognition, language modelling, adaptive robotics, modelling dynamic financial and socio-economic systems, and ecological modelling, are covered.
An important new feature of the book is the attempt to connect different structural and functional levels of a complex, intelligent system, looking for inspiration from functional relationships in natural systems, such as the genetic and the brain activity.
Overall, the book is more about problem solving and intelligent systems, than about mathematical proofs of theoretical models. Additional resources for practical model validation and system creation are attached as programs in the Appendix. Data, programs, colour figures and .ppt slides are available from: http://www.kedri.info/ and http://www.theneucom.com.
"This book is an important update on the first edition, taking account of exciting new developments in adaptive evolving systems. It is a very important book, and Nik should be congratulated on letting his enthusiasm shine through, but at the same time keeping his expertise as the ultimate guide. A must for all in the field!"
Professor John G Taylor, Kingβs College London
"This second edition provides fully integrated, up-to-date support for knowledge-based computing in a broad range of applications by students and professionals".
Professor Walter J Freeman,University of California at Berkeley
β¦ Table of Contents
Front Matter....Pages I-XXI
Front Matter....Pages 1-14
Feature Selection, Model Creation, and Model Validation....Pages 15-52
Evolving Connectionist Methods for Unsupervised Learning....Pages 53-82
Evolving Connectionist Methods for Supervised Learning....Pages 83-126
Brain Inspired Evolving Connectionist Models....Pages 127-140
Evolving Neuro-Fuzzy Inference Models....Pages 141-176
Population-Generation-Based Methods: Evolutionary Computation....Pages 177-202
Evolving Integrated Multimodel Systems....Pages 203-228
Front Matter....Pages 229-229
Adaptive Modelling and Knowledge Discovery in Bioinformatics....Pages 231-273
Dynamic Modelling of Brain Functions and Cognitive Processes....Pages 275-302
Modelling the Emergence of Acoustic Segments in Spoken Languages....Pages 303-323
Evolving Intelligent Systems for Adaptive Speech Recognition....Pages 325-340
Evolving Intelligent Systems for Adaptive Image Processing....Pages 341-359
Evolving Intelligent Systems for Adaptive Multimodal Information Processing....Pages 361-380
Evolving Intelligent Systems for Robotics and Decision Support....Pages 381-391
What Is Next: Quantum Inspired Evolving Intelligent Systems?....Pages 393-403
Back Matter....Pages 405-457
β¦ Subjects
Control Engineering
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