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Langue: anglais
Edité par Springer Vieweg 2014-02, 2014
ISBN 10 : 3658049367 ISBN 13 : 9783658049362
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Langue: anglais
Edité par Vieweg + Teubner Verlag, 2014
ISBN 10 : 3658049367 ISBN 13 : 9783658049362
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 2014 edition. 188 pages. 8.25x5.75x0.50 inches. In Stock.
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Ajouter au panierEtat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Langue: anglais
Edité par Springer Fachmedien Wiesbaden, 2014
ISBN 10 : 3658049367 ISBN 13 : 9783658049362
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain - at least to some extent. Therefore three suitable machine learning algorithms are selected - algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the applicability of the approach, while the biological plausibility is discussed in retrospect.
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Ajouter au panierPaperback. Etat : Like New. Like New. book.
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Ajouter au panierBrand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Vendeur : Buchpark, Trebbin, Allemagne
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain ¿ at least to some extent. Therefore three suitable machine learning algorithms are selected ¿ algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the applicability of the approach, while the biological plausibility is discussed in retrospect.
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Langue: anglais
Edité par Springer Verlag, Singapore, Singapore, 2020
ISBN 10 : 9813299924 ISBN 13 : 9789813299924
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Ajouter au panierPaperback. Etat : new. Paperback. This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Langue: anglais
Edité par Springer Verlag, Singapore, Singapore, 2019
ISBN 10 : 9813299894 ISBN 13 : 9789813299894
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Ajouter au panierHardcover. Etat : new. Hardcover. This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Ajouter au panierEtat : New. pp. X, 286 72 illus., 55 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 210,37
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Langue: anglais
Edité par Springer Verlag, Singapore, Singapore, 2024
ISBN 10 : 9819997178 ISBN 13 : 9789819997176
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Ajouter au panierHardcover. Etat : new. Hardcover. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Langue: anglais
Edité par Springer Nature Singapore, Springer Nature Singapore Nov 2020, 2020
ISBN 10 : 9813299924 ISBN 13 : 9789813299924
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 192,59
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks.The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.