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Analysis and Design of Machine Learning Techniques

Evolutionary Solutions for Regression, Prediction, and Control Problems

  • Book
  • © 2014

Overview

Authors:
  1. Patrick Stalph

    1. Lehrstuhl für kognitive Modellierung, Universität Tübingen, Tübingen, Germany

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  • Publication in the field of technical sciences
  • Includes supplementary material: sn.pub/extras

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Table of contents (10 chapters)

  1. Front Matter

    Pages I-XIX
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  2. Introduction and Motivation

    • Patrick Stalph
    Pages 1-8

  3. Background

    1. Front Matter

      Pages 9-9
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    2. Introduction to Function Approximation and Regression

      • Patrick Stalph
      Pages 11-28

    3. Elementary Features of Local Learning Algorithms

      • Patrick Stalph
      Pages 29-39

    4. Algorithmic Description of XCSF

      • Patrick Stalph
      Pages 41-53

  4. Analysis and Enhancements of XCSF

    1. Front Matter

      Pages 55-55
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    2. How and Why XCSF works

      • Patrick Stalph
      Pages 57-62

    3. Evolutionary Challenges for XCSF

      • Patrick Stalph
      Pages 63-83

  5. Control Applications in Robotics

    1. Front Matter

      Pages 85-85
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    2. Basics of Kinematic Robot Control

      • Patrick Stalph
      Pages 87-100

    3. Learning Directional Control of an Anthropomorphic Arm

      • Patrick Stalph
      Pages 101-123

    4. Visual Servoing for the iCub

      • Patrick Stalph
      Pages 125-135

    5. Summary and Conclusion

      • Patrick Stalph
      Pages 137-143

  6. Back Matter

    Pages 145-155
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Keywords

About this book

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.

Authors and Affiliations

  • Lehrstuhl für kognitive Modellierung, Universität Tübingen, Tübingen, Germany

    Patrick Stalph

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.

Bibliographic Information

  • Book Title: Analysis and Design of Machine Learning Techniques

  • Book Subtitle: Evolutionary Solutions for Regression, Prediction, and Control Problems

  • Authors: Patrick Stalph

  • DOI: https://doi.org/10.1007/978-3-658-04937-9

  • Publisher: Springer Vieweg Wiesbaden

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Fachmedien Wiesbaden 2014

  • Softcover ISBN: 978-3-658-04936-2Published: 17 February 2014

  • eBook ISBN: 978-3-658-04937-9Published: 06 February 2014

  • Edition Number: 1

  • Number of Pages: XIX, 155

  • Number of Illustrations: 62 b/w illustrations

  • Topics: Control, Robotics, Mechatronics, Computer Science, general, Neurobiology

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