LEADER 01512nam 2200349Ia 450 001 996386788003316 005 20221103135519.0 035 $a(CKB)4940000000082400 035 $a(EEBO)2264217462 035 $a(OCoLC)16412151 035 $a(EXLCZ)994940000000082400 100 $a19870810d1649 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 02$aA Serious and faithfull representation of the judgments of ministers of the Gospel within the the province of London$b[electronic resource] $econtained in a letter from the to the general and his councell of war /$fdelivered to His Excellence by some of the subscribers, Ian. 18, 1649 210 $a[Edinburgh] $cPrinted at London, and re-printed at Edinburgh by Evan Tyler ...$d1649 215 $a[2], 14 p 300 $aContains on p. 13-14 a list of 47 subscribers to the "Representation", headed by Thomas Gataker. 300 $aReproduction of original in the Union Theological Seminary Library, New York. 320 $aIncludes bibliographical references. 330 $aeebo-0160 606 $aChurch and state$zEngland 607 $aGreat Britain$xHistory$yCivil War, 1642-1649 615 0$aChurch and state 701 $aGataker$b Thomas$f1574-1654.$0825489 801 0$bEAJ 801 1$bEAJ 801 2$bWaOLN 906 $aBOOK 912 $a996386788003316 996 $aA Serious and faithfull representation of the judgments of ministers of the Gospel within the the province of London$92358386 997 $aUNISA 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. 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