rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Evolving Connectionist Systems
    sub title: The Knowledge Engineering Approach
  name:
    name part: Kasabov, Nikola.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 451 p. 185 illus. online resource.
  abstract: 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…
  table of contents: Evolving 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?.
  note: by Nikola Kasabov.
  subject:
    topic: Computer science
  subject:
    topic: Software engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer simulation
  subject:
    topic: Pattern recognition
  subject:
    topic: Bioinformatics
  subject:
    topic: Control engineering
  subject:
    topic: Robotics
  subject:
    topic: Mechatronics
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Software Engineering
  subject:
    topic: Simulation and Modeling
  subject:
    topic: Pattern Recognition
  subject:
    topic: Bioinformatics
  subject:
    topic: Control, Robotics, Mechatronics
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781846283475
  identifier: http://dx.doi.org/10.1007/978-1-84628-347-5
  record info:
    record creation date: 100301
    record change date: 20151204180922.0
    record identifier: 978-1-84628-347-5