LEADER 00933nam 2200265 450 001 9910413159003321 005 20200831094843.0 010 $a9788832827262 100 $a20200831d2020----u y0engy50 ba 101 1 $aita$ceng 102 $aIT 105 0 $a 00 200 1 $aMadri di saggezza$ela filosofia e la politica degli studi matriarcali moderni$fHeide Goettner - Abendroth$ga cura di Luciana Percovich$gtraduzione di Alberto Castagnola 210 $aRoma$cCastelvecchi$d2020 215 $a60 p.$d22 cm 225 $a<>navi 454 0$12001$a<>philosophy and politics of modern matriarcal studies$91755480 610 0 $aDonne$aPosizione sociale 676 $a305.42$v22$zita 700 1$aGoettner - Abendroth,$bHeide$0787717 912 $a9910413159003321 952 $a305.42 GOE 1$b7268$fbfs 959 $aBFS 996 $aPhilosophy and politics of modern matriarcal studies$91755480 997 $aUNINA LEADER 03742nam 2200529Ia 450 001 9910299714603321 005 20200520144314.0 010 $a3-642-37846-3 024 7 $a10.1007/978-3-642-37846-1 035 $a(OCoLC)854557929 035 $a(MiFhGG)GVRL6XIV 035 $a(CKB)2670000000422301 035 $a(MiAaPQ)EBC1398775 035 $a(EXLCZ)992670000000422301 100 $a20130730d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aMultidimensional particle swarm optimization for machine learning and pattern recognition /$fSerkan Kiranyaz, Turker Ince, Moncef Gabbouj 205 $a1st ed. 2014. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (xxviii, 321 pages) $cillustrations (some color) 225 0 $aAdaptation, learning, and optimization ;$v15 300 $a"ISSN: 1867-4534." 311 $a3-642-43762-1 311 $a3-642-37845-5 320 $aIncludes bibliographical references. 327 $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. 330 $aFor many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.   After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.   The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications. 410 0$aAdaptation, learning and optimization ;$vv. 15. 606 $aParticles$xOptical properties 606 $aOptical pattern recognition 615 0$aParticles$xOptical properties. 615 0$aOptical pattern recognition. 676 $a006.3 700 $aKiranyaz$b Serkan$0924538 701 $aInce$b Turker$01750785 701 $aGabbouj$b Moncef$01631582 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299714603321 996 $aMultidimensional particle swarm optimization for machine learning and pattern recognition$94185456 997 $aUNINA