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020 _a9781846283475
_99781846283475
024 7 _a10.1007/9781846283475
_2doi
035 _avtls000343818
039 9 _a201509030356
_bVLOAD
_c201405050258
_dVLOAD
_y201402061205
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQ334-342
100 1 _aKasabov, Nikola.
_eautor
_9305286
245 1 0 _aEvolving Connectionist Systems :
_bThe Knowledge Engineering Approach /
_cby Nikola Kasabov.
264 1 _aLondon :
_bSpringer London,
_c2007.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
500 _aSpringer eBooks
505 0 _aEvolving Connectionist Methods -- Feature Selection, Model Creation, and Model Validation -- Evolving Connectionist Methods for Unsupervised Learning -- Evolving Connectionist Methods for Supervised Learning -- Brain Inspired Evolving Connectionist Models -- Evolving Neuro-Fuzzy Inference Models -- Population-Generation-Based Methods: Evolutionary Computation -- Evolving Integrated Multimodel Systems -- Evolving Intelligent Systems -- Adaptive Modelling and Knowledge Discovery in Bioinformatics -- Dynamic Modelling of Brain Functions and Cognitive Processes -- Modelling the Emergence of Acoustic Segments in Spoken Languages -- Evolving Intelligent Systems for Adaptive Speech Recognition -- Evolving Intelligent Systems for Adaptive Image Processing -- Evolving Intelligent Systems for Adaptive Multimodal Information Processing -- Evolving Intelligent Systems for Robotics and Decision Support -- What Is Next: Quantum Inspired Evolving Intelligent Systems?.
520 _aEvolving 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, King’s 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
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781846283451
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84628-347-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c291588
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