TY - GEN digital ID - 131518460 TI - Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition AU - Kiranyaz, Serkan AU - Ince, Turker AU - Gabbouj, Moncef PY - 2014 SN - 9783642378461 PB - Berlin, Heidelberg Springer DB - UniCat KW - Electrical engineering KW - Applied physical engineering KW - Computer science KW - Artificial intelligence. Robotics. Simulation. Graphics KW - Computer. Automation KW - patroonherkenning KW - neuronale netwerken KW - fuzzy logic KW - cybernetica KW - machine learning KW - computers KW - informatica KW - multimedia KW - KI (kunstmatige intelligentie) KW - ingenieurswetenschappen KW - computerkunde KW - elektrotechniek KW - robots UR - https://www.unicat.be/uniCat?func=search&query=sysid:131518460 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 -