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  • BSZ  (3)
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  • 1
    UID:
    (DE-627)1605937061
    Umfang: XXVIII, 321 S. , Ill., graph. Darst. , 235 mm x 155 mm
    ISBN: 9783642378454 , 3642378455
    Serie: Adaptation, learning and optimization 15
    Weitere Ausg.: 9783642378461
    Weitere Ausg.: Online-Ausg. Kiranyaz, Serkan Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition Berlin, Heidelberg : Springer Berlin Heidelberg, 2014 9783642378461
    Sprache: Englisch
    Schlagwort(e): Partikel-Schwarm-Optimierung ; Maschinelles Lernen ; Mustererkennung
    URL: Cover
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    UID:
    (DE-627)1746252340
    Umfang: 1 online resource (343 pages)
    Ausgabe: 1st ed.
    ISBN: 9783642378461
    Serie: Adaptation, Learning, and Optimization Ser. v.15
    Inhalt: This book explores multidimensional particle swarm optimization, a technique developed by the authors and presented in a well-defined algorithmic approach. All featured applications are supported with fully documented source code as well as sample datasets.
    Inhalt: Intro -- Preface -- Abstract -- Contents -- Acronyms -- Tables -- Figures -- 1 Introduction -- 1.1…Optimization Era -- 1.2…Key Issues -- 1.3…Synopsis of the Book -- References -- 2 Optimization Techniques: An Overview -- 2.1…History of Optimization -- 2.2…Deterministic and Analytic Methods -- 2.2.1 Gradient Descent Method -- 2.2.2 Newton--Raphson Method -- 2.2.3 Nelder--Mead Search Method -- 2.3…Stochastic Methods -- 2.3.1 Simulated Annealing -- 2.3.2 Stochastic Approximation -- 2.4…Evolutionary Algorithms -- 2.4.1 Genetic Algorithms -- 2.4.2 Differential Evolution -- References -- 3 Particle Swarm Optimization -- 3.1…Introduction -- 3.2…Basic PSO Algorithm -- 3.3…Some PSO Variants -- 3.3.1 Tribes -- 3.3.2 Multiswarms -- 3.4…Applications -- 3.4.1 Nonlinear Function Minimization -- 3.4.2 Data Clustering -- 3.4.3 Artificial Neural Networks -- 3.4.3.1 An Overview -- 3.4.3.2 BP versus PSO: Comparative Performance Evaluation Over Medical Datasets -- 3.5…Programming Remarks and Software Packages -- References -- 4 Multi-dimensional Particle Swarm Optimization -- 4.1…The Need for Multi-dimensionality -- 4.2…The Basic Idea -- 4.3…The MD PSO Algorithm -- 4.4…Programming Remarks and Software Packages -- 4.4.1 MD PSO Operation in PSO_MDlib Application -- 4.4.2 MD PSO Operation in PSOTestApp Application -- References -- 5 Improving Global Convergence -- 5.1…Fractional Global Best Formation -- 5.1.1 The Motivation -- 5.1.2 PSO with FGBF -- 5.1.3 MD PSO with FGBF -- 5.1.4 Nonlinear Function Minimization -- 5.2…Optimization in Dynamic Environments -- 5.2.1 Dynamic Environments: The Test Bed -- 5.2.2 Multiswarm PSO -- 5.2.3 FGBF for the Moving Peak Benchmark for MPB -- 5.2.4 Optimization over Multidimensional MPB -- 5.2.5 Performance Evaluation on Conventional MPB -- 5.2.6 Performance Evaluation on Multidimensional MPB -- 5.3…Who Will Guide the Guide?.
    Anmerkung: Description based on publisher supplied metadata and other sources
    Weitere Ausg.: 9783642437625
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe 9783642437625
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 3
    Online-Ressource
    Online-Ressource
    Berlin, Heidelberg : Springer Berlin Heidelberg
    UID:
    (DE-627)1652963731
    Umfang: Online-Ressource (XXVIII, 321 p. 95 illus., 78 illus. in color, online resource)
    ISBN: 9783642378461
    Serie: Adaptation, Learning, and Optimization 15
    Inhalt: 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
    Anmerkung: Description based upon print version of record , Chap. 1 IntroductionChap. 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.
    Weitere Ausg.: 9783642378454
    Weitere Ausg.: Druckausg. Kiranyaz, Serkan Multidimensional particle swarm optimization for machine learning and pattern recognition Berlin : Springer, 2014 9783642378454
    Weitere Ausg.: 3642378455
    Sprache: Englisch
    Schlagwort(e): Partikel-Schwarm-Optimierung ; Maschinelles Lernen ; Mustererkennung
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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