000 | 03926nam a22005295i 4500 | ||
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001 | 978-3-642-37846-1 | ||
003 | DE-He213 | ||
005 | 20140220082517.0 | ||
007 | cr nn 008mamaa | ||
008 | 130716s2014 gw | s |||| 0|eng d | ||
020 |
_a9783642378461 _9978-3-642-37846-1 |
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024 | 7 |
_a10.1007/978-3-642-37846-1 _2doi |
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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. 95 illus., 78 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 | _aEngineering. | |
650 | 0 | _aComputer 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. |
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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 | ||
999 |
_c93216 _d93216 |