Analysis Design Machine Learning by Stalph Patrick (18 results)

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Paperback. Condition: new. Paperback. 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. Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condition: New. Num Pages: 174 pages, 62 black & white illustrations, biography. BIC Classification: PSAN; TJFM; UYQM. Category: (P) Professional & Vocational. Dimension: 210 x 148 x 10. Weight in Grams: 239. . 2014. Paperback. . . . .

Condition: New. Num Pages: 174 pages, 62 black & white illustrations, biography. BIC Classification: PSAN; TJFM; UYQM. Category: (P) Professional & Vocational. Dimension: 210 x 148 x 10. Weight in Grams: 239. . 2014. Paperback. . . . . Books ship from the US and Ireland.

Paperback. Condition: Brand New. 2014 edition. 188 pages. 8.25x5.75x0.50 inches. In Stock.

Taschenbuch. Condition: 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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Paperback. Condition: new. Paperback. 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. Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 176 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Publication in the field of technical sciencesIntroduction and Motivation.- Introduction to Function Approximation and Regression.- Elementary Features of Local Learning Algorithms.- Algorithmic Description of XCSF.- How and Why XCSF works.- Evolutionar.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VS Verlag für Sozialw., Abraham-Lincoln-Straße 46, 65189 Wiesbaden 176 pp. Englisch.