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2013


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2008


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GNU Octave Manual Version 3

John W. Eaton, David Bateman, Soren Hauberg

Network Theory Ltd., October 2008 (book)

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Publishers site GNU Octave [BibTex]

2008


Publishers site GNU Octave [BibTex]


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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.

ei

Web [BibTex]

Web [BibTex]

2006


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Semi-Supervised Learning

Chapelle, O., Schölkopf, B., Zien, A.

pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Abstract
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

ei

Web [BibTex]

2006


Web [BibTex]


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Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

ei

Web [BibTex]

Web [BibTex]

2003


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Magnetism and the Microstructure of Ferromagnetic Solids

Kronmüller, H., Fähnle, M.

pages: 432 p., 1st ed., Cambridge University Press, Cambridge, 2003 (book)

mms

[BibTex]

2003


[BibTex]


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(book)

[BibTex]