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Analysis and design of machine learning techniques [electronic resource] : evolutionary solutions for regression, prediction, and control problems

Analysis and design of machine learning techniques [electronic resource] : evolutionary solutions for regression, prediction, and control problems

Material type
E-Book(소장)
Personal Author
Stalph, Patrick.
Title Statement
Analysis and design of machine learning techniques [electronic resource] : evolutionary solutions for regression, prediction, and control problems / Patrick Stalph.
Publication, Distribution, etc
Wiesbaden :   Springer Fachmedien Wiesbaden :   Imprint: Springer Vieweg,   2014.  
Physical Medium
1 online resource (xix, 155 p.) : ill.
ISBN
9783658049379
요약
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 of Cognitive Modeling, which is led by Prof. Butz at the University of Tübingen.
General Note
Title from e-Book title page.  
Content Notes
Introduction and Motivation -- Introduction to Function Approximation and Regression -- Elementary Features of Local Learning Algorithms -- Algorithmic Description of XCSF -- How and Why XCSF works -- Evolutionary Challenges for XCSF -- Basics of Kinematic Robot Control -- Learning Directional Control of an Anthropomorphic Arm -- Visual Servoing for the iCub -- Summary and Conclusion.
Bibliography, Etc. Note
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Machine learning. Robots --Control systems --Design and construction. Motor ability. Robotics.
Short cut
URL
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001 000046050074
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006 m d
007 cr
008 201005s2014 gw a ob 000 0 eng d
020 ▼a 9783658049379
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.31 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31
100 1 ▼a Stalph, Patrick.
245 1 0 ▼a Analysis and design of machine learning techniques ▼h [electronic resource] : ▼b evolutionary solutions for regression, prediction, and control problems / ▼c Patrick Stalph.
260 ▼a Wiesbaden : ▼b Springer Fachmedien Wiesbaden : ▼b Imprint: Springer Vieweg, ▼c 2014.
300 ▼a 1 online resource (xix, 155 p.) : ▼b ill.
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a Introduction and Motivation -- Introduction to Function Approximation and Regression -- Elementary Features of Local Learning Algorithms -- Algorithmic Description of XCSF -- How and Why XCSF works -- Evolutionary Challenges for XCSF -- Basics of Kinematic Robot Control -- Learning Directional Control of an Anthropomorphic Arm -- Visual Servoing for the iCub -- Summary and Conclusion.
520 ▼a 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 of Cognitive Modeling, which is led by Prof. Butz at the University of Tübingen.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Machine learning.
650 0 ▼a Robots ▼x Control systems ▼x Design and construction.
650 0 ▼a Motor ability.
650 0 ▼a Robotics.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-658-04937-9
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 006.31 Accession No. E14034934 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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