Analysis and Design of Machine Learning Techniques | Evolutionary Solutions for Regression, Prediction, and Control Problems

Patrick Stalph

ISBN 10: 3658049367 / ISBN 13: 9783658049362

Published by Springer Fachmedien Wiesbaden, 2014

About this Item

Analysis and Design of Machine Learning Techniques | Evolutionary Solutions for Regression, Prediction, and Control Problems | Patrick Stalph | Taschenbuch | xix | Englisch | 2014 | Springer Fachmedien Wiesbaden | EAN 9783658049362 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 105490527

Bibliographic Details

• Title: Analysis and Design of Machine Learning Techniques | Evolutionary Solutions for Regression, Prediction, and Control Problems
• Publisher: Springer Fachmedien Wiesbaden
• Publication Date: 2014
• Language: English
• ISBN 10: 3658049367
• ISBN 13: 9783658049362
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 10D millimeters 210 millimeters width by 148 millimeters height by 10 millimeters depth
• Weight: 236 grams
• Seller catalogs:
  • Bücher

About this title

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.

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.