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Centripetal Accelerated Particle Swarm Optimization And Applications: CAPSO and its Applications in Machine Learning - Brossura

 
9783639707076: Centripetal Accelerated Particle Swarm Optimization And Applications: CAPSO and its Applications in Machine Learning

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Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton’s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces.

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Dr. Zahra Beheshti received her BSc and MSc in Computer Engineering from Islamic Azad University Najafabad branch (IAUN), Iran and PhD in Artificial Intelligence from Universiti Teknologi Malaysia (UTM), Malaysia. Her current research interests include Artificial Intelligence and Soft Computing.

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Zahra Beheshti|Siti Mariyam Shamsuddin
Editore: Scholars\' Press, 2014
ISBN 10: 3639707079 ISBN 13: 9783639707076
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Beheshti ZahraDr. Zahra Beheshti received her BSc and MSc in Computer Engineering from Islamic Azad University Najafabad branch (IAUN), Iran and PhD in Artificial Intelligence from Universiti Teknologi Malaysia (UTM), Malaysia. Her c. Codice articolo 4999047

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Zahra Beheshti
Editore: Scholars' Press Apr 2014, 2014
ISBN 10: 3639707079 ISBN 13: 9783639707076
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. 196 pp. Englisch. Codice articolo 9783639707076

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Zahra Beheshti
Editore: Scholars' Press, 2014
ISBN 10: 3639707079 ISBN 13: 9783639707076
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. Codice articolo 9783639707076

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Zahra Beheshti
Editore: Scholars' Press Apr 2014, 2014
ISBN 10: 3639707079 ISBN 13: 9783639707076
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton¿s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch. Codice articolo 9783639707076

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Beheshti, Zahra, Shamsuddin, Siti Mariyam
Editore: Scholars* Press, 2014
ISBN 10: 3639707079 ISBN 13: 9783639707076
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