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Analysis and Design of Machine Learning Techniques: Evolutionary Solutions for Regression, Prediction, and Control Problems - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Patrick Stalph was a Ph.D. student at the chair of Cognitive Modeling, which is led by Prof. Butz at the University of Tübingen.
From the Back Cover
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.
Contents
- How do humans learn their motor skills
- Evolutionarymachinelearningalgorithms
- Applicationtosimulatedrobots
Target Groups
- Researchers interested in artificial intelligence, cognitive sciences or robotics
- Roboticists interested in integrating machine learning
About the Author
Patrick Stalph was a Ph.D. student at the chair ofCognitive Modeling, which is led by Prof. Butz at the University of Tübingen.
"About this title" may belong to another edition of this title.
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Analysis and Design of Machine Learning Techniques (Paperback)
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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. Seller Inventory # 9783658049362
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Analysis and Design of Machine Learning Techniques
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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. Seller Inventory # 9783658049362
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Analysis and Design of Machine Learning Techniques : Evolutionary Solutions for Regression, Prediction, and Control Problems
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Analysis and Design of Machine Learning Techniques
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