000 03932nam a22005415i 4500
001 978-3-642-37846-1
003 DE-He213
005 20200421111839.0
007 cr nn 008mamaa
008 130716s2014 gw | s |||| 0|eng d
020 _a9783642378461
_9978-3-642-37846-1
024 7 _a10.1007/978-3-642-37846-1
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKiranyaz, Serkan.
_eauthor.
245 1 0 _aMultidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition
_h[electronic resource] /
_cby Serkan Kiranyaz, Turker Ince, Moncef Gabbouj.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXXVIII, 321 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdaptation, Learning, and Optimization,
_x1867-4534 ;
_v15
505 0 _aChap. 1 Introduction -- Chap. 2 Optimization Techniques -- Chap. 3 Particle Swarm Optimization -- Chap. 4 Multidimensional Particle Swarm Optimization -- Chap. 5 Improving Global Convergence -- Chap. 6 Dynamic Data Clustering -- Chap. 7 Evolutionary Artificial Neural Networks -- Chap. 8 Personalized ECG Classification -- Chap. 9 Image Classification Through a Collective Network of Binary Classifiers -- Chap. 10 Evolutionary Feature Synthesis for Image Retrieval.
520 _aFor 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.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 0 _aElectrical engineering.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Intelligence.
650 2 4 _aElectrical Engineering.
700 1 _aInce, Turker.
_eauthor.
700 1 _aGabbouj, Moncef.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642378454
830 0 _aAdaptation, Learning, and Optimization,
_x1867-4534 ;
_v15
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-37846-1
912 _aZDB-2-ENG
942 _cEBK
999 _c55485
_d55485