9783642553363 - growing adaptive machines: combining development and learning in artificial neural networks (studies in computational intelligence, 557, band 557) (3 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2014
Serie: Buch 85 von 538 - Studies in Computational Intelligence
- Hardcover
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EUR 116,65
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Zustand: New. In English.
Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks
Kowaliw, Taras (Editor)/ Bredeche, Nicolas (Editor)/ Doursat, René (Editor)
Sprache: Englisch
Verlag: Springer-Verlag New York Inc, 2014
Serie: Buch 85 von 538 - Studies in Computational Intelligence
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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Hardcover. Zustand: Brand New. 261 pages. 9.75x6.75x0.75 inches. In Stock.
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Sprache: Englisch
Verlag: Springer, Springer, 2014
Serie: Buch 85 von 538 - Studies in Computational Intelligence
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 106,99
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited succes…s. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks.The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the Hyper NEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the design of virtual multi-component robots and morphologies and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines.This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning.
