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2015


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Kernel methods in medical imaging

Charpiat, G., Hofmann, M., Schölkopf, B.

In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

Web link (url) [BibTex]

2015

Web link (url) [BibTex]


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Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L. J., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

[BibTex]

[BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

DOI [BibTex]

2012


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Expectation-Maximization methods for solving (PO)MDPs and optimal control problems

Toussaint, M., Storkey, A., Harmeling, S.

In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

PDF [BibTex]

2012

PDF [BibTex]


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Active Learning Methods in Classification of Remote Sensing Images

Bruzzone, L., Persello, C., Demir, B.

In Signal and Image Processing for Remote Sensing, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

[BibTex]

[BibTex]


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Inferential structure determination from NMR data

Habeck, M.

In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

[BibTex]

[BibTex]


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Robot Learning

Sigaud, O., Peters, J.

In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

Web [BibTex]

Web [BibTex]


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Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Abstract
As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

Web DOI [BibTex]

Web DOI [BibTex]


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Higher-Order Tensors in Diffusion MRI

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

[BibTex]

[BibTex]

2004


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Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis

Aigner, T., Saas, J., Zien, A., Zimmer, R., Gebhard, P., Knorr, T.

In Volume 1: Cellular and Molecular Tools, pages: 109-128, (Editors: Sabatini, M., P. Pastoureau and F. De Ceuninck), Humana Press, July 2004 (inbook)

Abstract
The regulation of chondrocytes in osteoarthritic cartilage and the expression of specific gene products by these cells during early-onset and late-stage osteoarthritis are not well characterized. With the introduction of cDNA array technology, the measurement of thousands of different genes in one small tissue sample can be carried out. Interpretation of gene expression analyses in articular cartilage is aided by the fact that this tissue contains only one cell type in both normal and diseased conditions. However, care has to be taken not to over- and misinterpret results, and some major challenges must be overcome in order to utilize the potential of this technology properly in the field of osteoarthritis.

Web [BibTex]

2004

Web [BibTex]


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Distributed Command Execution

Stark, S., Berlin, M.

In BSD Hacks: 100 industrial-strength tips & tools, pages: 152-152, (Editors: Lavigne, Dru), O’Reilly, Beijing, May 2004 (inbook)

Abstract
Often you want to execute a command not only on one computer, but on several at once. For example, you might want to report the current statistics on a group of managed servers or update all of your web servers at once.

[BibTex]

[BibTex]


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Local Alignment Kernels for Biological Sequences

Vert, J., Saigo, H., Akutsu, T.

In Kernel Methods in Computational Biology, pages: 131-153, MIT Press, Cambridge, MA,, 2004 (inbook)

Web [BibTex]

Web [BibTex]


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Gaussian Processes in Machine Learning

Rasmussen, CE.

In 3176, pages: 63-71, Lecture Notes in Computer Science, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, 2004, Copyright by Springer (inbook)

Abstract
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Protein Classification via Kernel Matrix Completion

Kin, T., Kato, T., Tsuda, K.

In pages: 261-274, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

PDF [BibTex]

PDF [BibTex]


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Introduction to Statistical Learning Theory

Bousquet, O., Boucheron, S., Lugosi, G.

In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

PDF [BibTex]

PDF [BibTex]


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A Primer on Kernel Methods

Vert, J., Tsuda, K., Schölkopf, B.

In Kernel Methods in Computational Biology, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

PDF [BibTex]

PDF [BibTex]


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Concentration Inequalities

Boucheron, S., Lugosi, G., Bousquet, O.

In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

PDF [BibTex]

PDF [BibTex]


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Kernels for graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In pages: 155-170, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

PDF [BibTex]

PDF [BibTex]


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A primer on molecular biology

Zien, A.

In pages: 3-34, (Editors: Schoelkopf, B., K. Tsuda and J. P. Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

Abstract
Modern molecular biology provides a rich source of challenging machine learning problems. This tutorial chapter aims to provide the necessary biological background knowledge required to communicate with biologists and to understand and properly formalize a number of most interesting problems in this application domain. The largest part of the chapter (its first section) is devoted to the cell as the basic unit of life. Four aspects of cells are reviewed in sequence: (1) the molecules that cells make use of (above all, proteins, RNA, and DNA); (2) the spatial organization of cells (``compartmentalization''); (3) the way cells produce proteins (``protein expression''); and (4) cellular communication and evolution (of cells and organisms). In the second section, an overview is provided of the most frequent measurement technologies, data types, and data sources. Finally, important open problems in the analysis of these data (bioinformatics challenges) are briefly outlined.

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]