TY - BOOK ID - 8360748 TI - Handbook on neural information processing AU - Bianchini, Monica. AU - Maggini, Marco. AU - Lakhmi, C. Jain. PY - 2013 SN - 3642429890 3642366562 3642366570 PB - Heidelberg ; New York : Springer, DB - UniCat KW - Adaptive control systems. KW - Artificial intelligence. KW - Neural networks (Computer science). KW - Engineering & Applied Sciences KW - Computer Science KW - Neural networks (Computer science) KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Artificial neural networks KW - Nets, Neural (Computer science) KW - Networks, Neural (Computer science) KW - Neural nets (Computer science) KW - Engineering. KW - Computational intelligence. KW - Computational Intelligence. KW - Artificial Intelligence (incl. Robotics). KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Construction KW - Industrial arts KW - Technology KW - Natural computation KW - Artificial Intelligence. KW - Machine learning. UR - https://www.unicat.be/uniCat?func=search&query=sysid:8360748 AB - This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:                         Deep architectures                         Recurrent, recursive, and graph neural networks                         Cellular neural networks                         Bayesian networks                         Approximation capabilities of neural networks                         Semi-supervised learning                         Statistical relational learning                         Kernel methods for structured data                         Multiple classifier systems                         Self organisation and modal learning                         Applications to content-based image retrieval, text mining in large document collections, and bioinformatics   This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms. ER -