TY - BOOK ID - 19495359 TI - Multidimensional particle swarm optimization for machine learning and pattern recognition AU - Kiranyaz, Serkan. AU - Ince, Turker. AU - Gabbouj, Moncef. PY - 2013 SN - 3642378455 3642437621 3642378463 PB - New York : Springer, DB - UniCat KW - Particles KW - Optical pattern recognition. KW - Optical properties. KW - Computer science. KW - Artificial intelligence. KW - Computational intelligence. KW - Electrical engineering. KW - Computer Science. KW - Artificial Intelligence (incl. Robotics). KW - Computational Intelligence. KW - Electrical Engineering. KW - Electric engineering KW - Engineering KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing 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 - 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 - Informatics KW - Science KW - Optical data processing KW - Pattern perception KW - Perceptrons KW - Visual discrimination KW - Engineering. KW - Computer engineering. KW - Artificial Intelligence. KW - Computers KW - Construction KW - Industrial arts KW - Technology KW - Design and construction KW - Mathematical optimization. KW - Swarm intelligence. KW - Machine learning. KW - Pattern recognition systems. UR - https://www.unicat.be/uniCat?func=search&query=sysid:19495359 AB - For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach. After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets. The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications. ER -