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2005


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Some thoughts about Gaussian Processes

Chapelle, O.

NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

ei

PDF Web [BibTex]

2005


PDF Web [BibTex]


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Extension to Kernel Dependency Estimation with Applications to Robotics

BakIr, G.

Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

Abstract
Kernel Dependency Estimation(KDE) is a novel technique which was designed to learn mappings between sets without making assumptions on the type of the involved input and output data. It learns the mapping in two stages. In a first step, it tries to estimate coordinates of a feature space representation of elements of the set by solving a high dimensional multivariate regression problem in feature space. Following this, it tries to reconstruct the original representation given the estimated coordinates. This thesis introduces various algorithmic extensions to both stages in KDE. One of the contributions of this thesis is to propose a novel linear regression algorithm that explores low-dimensional subspaces during learning. Furthermore various existing strategies for reconstructing patterns from feature maps involved in KDE are discussed and novel pre-image techniques are introduced. In particular, pre-image techniques for data-types that are of discrete nature such as graphs and strings are investigated. KDE is then explored in the context of robot pose imitation where the input is a an image with a human operator and the output is the robot articulated variables. Thus, using KDE, robot pose imitation is formulated as a regression problem.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Geometrical aspects of statistical learning theory

Hein, M.

Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Implicit Surfaces For Modelling Human Heads

Steinke, F.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

ei

[BibTex]

[BibTex]


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Machine Learning Methods for Brain-Computer Interdaces

Lal, TN.

Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Building Sparse Large Margin Classifiers

Wu, M., Schölkopf, B., BakIr, G.

The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Learning from Labeled and Unlabeled Data on a Directed Graph

Zhou, D.

The 22nd International Conference on Machine Learning, August 2005 (talk)

Abstract
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.

ei

PDF [BibTex]

PDF [BibTex]


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Machine-Learning Approaches to BCI in Tübingen

Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M.

Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

ei

[BibTex]

[BibTex]


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Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, TG.

Biologische Kybernetik, Eberhard-Karls University Tübingen, Tübingen, Germany, May 2005 (diplomathesis)

Abstract
A common task in psychophysics is to measure the psychometric function. A psychometric function can be described by its shape and four parameters: offset or threshold, slope or width, false alarm rate or chance level and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. A new Bayesian adaptive psychometric method placing trials by minimising the expected entropy of the posterior probabilty dis- tribution over a set of possible stimuli is introduced. The method is more flexible, faster and at least as efficient as the established method (Kontsevich and Tyler, 1999). Comparably accurate (2dB) threshold and slope estimates can be obtained after about 30 and 500 trials, respectively. By using a dynamic termination criterion the efficiency can be further improved. The method can be applied to all experimental designs including yes/no designs and allows acquisition of any set of free parameters. By weighting the importance of parameters one can include nuisance parameters and adjust the relative expected errors. Use of nuisance parameters may lead to more accurate estimates than assuming a guessed fixed value. Block designs are supported and do not harm the performance if a sufficient number of trials are performed. The method was evaluated by computer simulations in which the role of parametric assumptions, its robustness, the quality of different point estimates, the effect of dynamic termination criteria and many other settings were investigated.

ei

[BibTex]

[BibTex]


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Kernel Constrained Covariance for Dependence Measurement

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N.

AISTATS, January 2005 (talk)

Abstract
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables in all circumstances. Dependent random variables can result in a COCO which is arbitrarily close to zero when the source densities are highly non-smooth. All current kernel-based independence tests share this behaviour. We demonstrate exponential convergence between the population and empirical COCO. Finally, we use COCO as a measure of joint neural activity between voxels in MRI recordings of the macaque monkey, and compare the results to the mutual information and the correlation. We also show the effect of removing breathing artefacts from the MRI recording.

ei

PostScript [BibTex]

PostScript [BibTex]


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Support Vector Machines and Kernel Algorithms

Schölkopf, B., Smola, A.

In Encyclopedia of Biostatistics (2nd edition), Vol. 8, 8, pages: 5328-5335, (Editors: P Armitage and T Colton), John Wiley & Sons, NY USA, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Visual perception I: Basic principles

Wagemans, J., Wichmann, F., de Beeck, H.

In Handbook of Cognition, pages: 3-47, (Editors: Lamberts, K. , R. Goldstone), Sage, London, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Untersuchung über die Grenzflächenmagnetisierung an (FM) Kobalt- und (AFM) Eisen-Mangan-Schichten

Harlander, M.

Stuttgart, Universität Stuttgart, 2005 (mastersthesis)

mms

[BibTex]

[BibTex]


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Geckobot and waalbot: Small-scale wall climbing robots

Unver, O., Murphy, M., Sitti, M.

In Infotech@ Aerospace, pages: 6940, 2005 (incollection)

pi

[BibTex]

[BibTex]

2004


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Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

ei

PDF [BibTex]


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Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract
Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

ei

PDF [BibTex]


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Riemannian Geometry on Graphs and its Application to Ranking and Classification

Zhou, D.

June 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data.

ei

PDF [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.

ei

[BibTex]

[BibTex]


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Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

Zhou, D.

January 2004 (talk)

Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

ei

PDF [BibTex]


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Introduction to Category Theory

Bousquet, O.

Internal Seminar, January 2004 (talk)

Abstract
A brief introduction to the general idea behind category theory with some basic definitions and examples. A perspective on higher dimensional categories is given.

ei

PDF [BibTex]

PDF [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.

ei

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)

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning with Similarity and Dissimilarity Functions

von Luxburg, U.

pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

ei

PDF PostScript [BibTex]

PDF PostScript [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)

ei

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)

ei

PDF [BibTex]

PDF [BibTex]


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Classification and Feature Extraction in Man and Machine

Graf, AAB.

Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

ei

[BibTex]

[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)

ei

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)

ei

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.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Advanced Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, 2004 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Investigation of oxide layers in tunnel junctions

Amaladass, E. P.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchung der Desorptionskinetik von Metallhydriden in Bezug auf technische Anwendungen

von Zeppelin, F.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Towards Tractable Parameter-Free Statistical Learning (Phd Thesis)

D’Souza, A

Department of Computer Science, University of Southern California, Los Angeles, 2004, clmc (phdthesis)

am

link (url) [BibTex]

link (url) [BibTex]


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Dynamik von Wasserstoff in nanokristallinen Systemen

Stanik, E.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Inselwachstum auf Festkörperoberflächen unter Ionenbestrahlung

Frank, A.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Flusslinienverankerung in HTSL-Schichten mit kontrollierter Defektstruktur im Nanometerbereich

Leonhardt, S.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Investigation of the stability of metals on polymers

Amoako, G.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Flusslinienverankerung in Hochtemperatursupraleitern auf nanostrukturierten Substraten

Brück, S.

Universität Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Ionenstreuung mit Monolagen-Tiefenauflösung

Olliges, S.

Universität Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Computational approaches to motor learning by imitation

Schaal, S., Ijspeert, A., Billard, A.

In The Neuroscience of Social Interaction, (1431):199-218, (Editors: Frith, C. D.;Wolpert, D.), Oxford University Press, Oxford, 2004, clmc (inbook)

Abstract
Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking - indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.

am

link (url) [BibTex]

link (url) [BibTex]


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Untersuchungen zum Magnetismus von Clustern und Nanopartikeln und zum Einfluss der Wechselwirkung mit ihrer Umgebung

Fauth, Kai

Julius-Maximilians-Universität Würzburg, Würzburg, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Effect of Grain Boundary Phase Transitions on the Superplasticity in the Al-Zn System

Lopez, G.A., Straumal, B.B., Gust, W., Mittemeijer, E.J.

In Nanomaterials by Severe Plastic Deformation, pages: 642-647, Wiley-VCH Verlag, Weinheim, 2004 (incollection)

mms

[BibTex]

[BibTex]