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4 results (View BibTeX file of all listed publications)

2017

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Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF [BibTex]

2017

Peters, J., Janzing, D., Schölkopf, B. Elements of Causal Inference - Foundations and Learning Algorithms Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF [BibTex]

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New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B.

Dagstuhl Reports, 6(11):142-167, 2017 (book)

DOI [BibTex]

2008

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Machine Learning for Robotics: Learning Methods for Robot Motor Skills

Peters, J.

pages: 107 , (Editors: J Peters), VDM-Verlag, Saarbrücken, Germany, May 2008 (book)

Abstract
Autonomous robots have been a vision of robotics, artificial intelligence, and cognitive sciences. An important step towards this goal is to create robots that can learn to accomplish amultitude of different tasks triggered by environmental context and higher-level instruction. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s showed that handcrafted approaches do not suffice and that machine learning is needed. However, off the shelf learning techniques often do not scale into real-time or to the high-dimensional domains of manipulator and humanoid robotics. In this book, we investigate the foundations for a general approach to motor skilllearning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and executionis presented along with novel learning algorithms that can be applied in this setting. The resulting framework is shown to work well both in simulation and on real robots.

Web [BibTex]

2008

Peters, J. Machine Learning for Robotics: Learning Methods for Robot Motor Skills pages: 107 , (Editors: J Peters), VDM-Verlag, Saarbrücken, Germany, May 2008 (book)

Web [BibTex]

2002

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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Schölkopf, B., Smola, A.

pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Abstract
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Web [BibTex]

2002

Schölkopf, B., Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Web [BibTex]