Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Shajarisales, N. A Novel Causal Inference Method for Time Series Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)

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A global analysis of extreme events and consequences for the terrestrial carbon cycle

Zscheischler, J.

Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

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Zscheischler, J. A global analysis of extreme events and consequences for the terrestrial carbon cycle Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

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Development of advanced methods for improving astronomical images

Schmeißer, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

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Schmeißer, N. Development of advanced methods for improving astronomical images Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

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The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection

Lacosse, E.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

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Lacosse, E. The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

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Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size

Huang, B.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

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Huang, B. Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

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Analysis of Distance Functions in Graphs

Alamgir, M.

University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)

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Alamgir, M. Analysis of Distance Functions in Graphs University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)

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2004

Fast Binary and Multi-Output Reduced Set Selection

Weston, J., Bakir, G.

(132), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2004 (techreport)

Abstract

We propose fast algorithms for reducing the number of kernel evaluations in the testing phase for methods such as Support Vector Machines (SVM) and Ridge Regression (RR). For non-sparse methods such as RR this results in significantly improved prediction time. For binary SVMs, which are already sparse in their expansion, the pay off is mainly in the cases of noisy or large-scale problems. However, we then further develop our method for multi-class problems where, after choosing the expansion to find vectors which describe all the hyperplanes jointly, we again achieve significant gains.

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2004

Weston, J., Bakir, G. Fast Binary and Multi-Output Reduced Set Selection (132), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2004 (techreport)

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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W.

(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

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Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W. Joint Kernel Maps (131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

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Independent component analysis and beyond

Harmeling, S.

Biologische Kybernetik, Universität Potsdam, Potsdam, October 2004 (phdthesis)

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Harmeling, S. Independent component analysis and beyond Biologische Kybernetik, Universität Potsdam, Potsdam, October 2004 (phdthesis)

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Semi-Supervised Induction

Yu, K., Tresp, V., Zhou, D.

(141), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, August 2004 (techreport)

Abstract

Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

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Yu, K., Tresp, V., Zhou, D. Semi-Supervised Induction (141), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, August 2004 (techreport)

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On Hausdorff Distance Measures

Shapiro, MD., Blaschko, MB.

Department of Computer Science, University of Massachusetts Amherst, August 2004 (techreport)

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Shapiro, MD., Blaschko, MB. On Hausdorff Distance Measures Department of Computer Science, University of Massachusetts Amherst, August 2004 (techreport)

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Object categorization with SVM: kernels for local features

Eichhorn, J., Chapelle, O.

(137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract

In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

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Eichhorn, J., Chapelle, O. Object categorization with SVM: kernels for local features (137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

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Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

(126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract

We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and one image classification problem.

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Hein, M., Bousquet, O. Hilbertian Metrics and Positive Definite Kernels on Probability Measures (126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

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Kernels, Associated Structures and Generalizations

Hein, M., Bousquet, O.

(127), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract

This paper gives a survey of results in the mathematical literature on positive definite kernels and their associated structures. We concentrate on properties which seem potentially relevant for Machine Learning and try to clarify some results that have been misused in the literature. Moreover we consider different lines of generalizations of positive definite kernels. Namely we deal with operator-valued kernels and present the general framework of Hilbertian subspaces of Schwartz which we use to introduce kernels which are distributions. Finally indefinite kernels and their associated reproducing kernel spaces are considered.

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Hein, M., Bousquet, O. Kernels, Associated Structures and Generalizations (127), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

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Triangle Fixing Algorithms for the Metric Nearness Problem

Dhillon, I., Sra, S., Tropp, J.

Univ. of Texas at Austin, June 2004 (techreport)

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Dhillon, I., Sra, S., Tropp, J. Triangle Fixing Algorithms for the Metric Nearness Problem Univ. of Texas at Austin, June 2004 (techreport)

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Exploration of combining Echo-State Network Learning with Recurrent Neural Network Learning techniques

Erhan, D.

Biologische Kybernetik, International University Bremen, Bremen, Germany, May 2004 (diplomathesis)

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Erhan, D. Exploration of combining Echo-State Network Learning with Recurrent Neural Network Learning techniques Biologische Kybernetik, International University Bremen, Bremen, Germany, May 2004 (diplomathesis)

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Kamerakalibrierung und Tiefenschätzung: Ein Vergleich von klassischer Bündelblockausgleichung und statistischen Lernalgorithmen

Sinz, FH.

Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Tübingen, Germany, March 2004 (techreport)

Abstract

Die Arbeit verleicht zwei Herangehensweisen an das Problem der Sch{\"a}tzung der r{\"a}umliche Position eines Punktes aus den Bildkoordinaten in zwei verschiedenen Kameras. Die klassische Methode der B{\"u}ndelblockausgleichung modelliert zwei Einzelkameras und sch{\"a}tzt deren {\"a}ußere und innere Orientierung mit einer iterativen Kalibrationsmethode, deren Konvergenz sehr stark von guten Startwerten abh{\"a}ngt. Die Tiefensch{\"a}tzung eines Punkts geschieht durch die Invertierung von drei der insgesamt vier Projektionsgleichungen der Einzalkameramodelle. Die zweite Methode benutzt Kernel Ridge Regression und Support Vector Regression, um direkt eine Abbildung von den Bild- auf die Raumkoordinaten zu lernen. Die Resultate zeigen, daß der Ansatz mit maschinellem Lernen, neben einer erheblichen Vereinfachung des Kalibrationsprozesses, zu h{\"o}heren Positionsgenaugikeiten f{\"u}hren kann.

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Sinz, FH. Kamerakalibrierung und Tiefenschätzung: Ein Vergleich von klassischer Bündelblockausgleichung und statistischen Lernalgorithmen Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Tübingen, Germany, March 2004 (techreport)

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Computational Analysis of Gene Expression Data

Zien, A.

(4), Biologische Kybernetik, March 2004 (phdthesis)

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Zien, A. Computational Analysis of Gene Expression Data (4), Biologische Kybernetik, March 2004 (phdthesis)

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Multivariate Regression with Stiefel Constraints

Bakir, G., Gretton, A., Franz, M., Schölkopf, B.

(128), MPI for Biological Cybernetics, Spemannstr 38, 72076, Tuebingen, 2004 (techreport)

Abstract

We introduce a new framework for regression between multi-dimensional spaces. Standard methods for solving this problem typically reduce the problem to one-dimensional regression by choosing features in the input and/or output spaces. These methods, which include PLS (partial least squares), KDE (kernel dependency estimation), and PCR (principal component regression), select features based on different a-priori judgments as to their relevance. Moreover, loss function and constraints are chosen not primarily on statistical grounds, but to simplify the resulting optimisation. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constraint. A major advantage of this approach is that the loss is no longer chosen according to the algorithmic requirements, but can be tailored to the characteristics of the task at hand; the features will then be optimal with respect to this objective. Our approach also allows for the possibility of using a regularizer in the optimization. Finally, by processing the observations sequentially, our algorithm is able to work on large scale problems.

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Bakir, G., Gretton, A., Franz, M., Schölkopf, B. Multivariate Regression with Stiefel Constraints (128), MPI for Biological Cybernetics, Spemannstr 38, 72076, Tuebingen, 2004 (techreport)

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The p53 Oligomerization Domain: Sequence-Structure Relationships and the Design and Characterization of Altered Oligomeric States

Davison, TS.

University of Toronto, Canada, University of Toronto, Canada, 2004 (phdthesis)

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Davison, TS. The p53 Oligomerization Domain: Sequence-Structure Relationships and the Design and Characterization of Altered Oligomeric States University of Toronto, Canada, University of Toronto, Canada, 2004 (phdthesis)

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Learning from Labeled and Unlabeled Data Using Random Walks

Zhou, D., Schölkopf, B.

Max Planck Institute for Biological Cybernetics, 2004 (techreport)

Abstract

We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. In this paper we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

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Zhou, D., Schölkopf, B. Learning from Labeled and Unlabeled Data Using Random Walks Max Planck Institute for Biological Cybernetics, 2004 (techreport)

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Behaviour and Convergence of the Constrained Covariance

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Schölkopf, B., Logothetis, N.

(130), MPI for Biological Cybernetics, 2004 (techreport)

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, which can make dependence hard to detect empirically. All current kernel-based independence tests share this behaviour. Finally, we demonstrate exponential convergence between the population and empirical COCO, which implies that COCO does not suffer from slow learning rates when used as a dependence test.

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Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Schölkopf, B., Logothetis, N. Behaviour and Convergence of the Constrained Covariance (130), MPI for Biological Cybernetics, 2004 (techreport)

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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)

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von Luxburg, U. Statistical Learning with Similarity and Dissimilarity Functions pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

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Confidence Sets for Ratios: A Purely Geometric Approach To Fieller’s Theorem

von Luxburg, U., Franz, V.

(133), Max Planck Institute for Biological Cybernetics, 2004 (techreport)

Abstract

We present a simple, geometric method to construct Fieller's exact confidence sets for ratios of jointly normally distributed random variables. Contrary to previous geometric approaches in the literature, our method is valid in the general case where both sample mean and covariance are unknown. Moreover, not only the construction but also its proof are purely geometric and elementary, thus giving intuition into the nature of the confidence sets.

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von Luxburg, U., Franz, V. Confidence Sets for Ratios: A Purely Geometric Approach To Fieller’s Theorem (133), Max Planck Institute for Biological Cybernetics, 2004 (techreport)

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Transductive Inference with Graphs

Zhou, D., Schölkopf, B.

Max Planck Institute for Biological Cybernetics, 2004, See the improved version Regularization on Discrete Spaces. (techreport)

Abstract

We propose a general regularization framework for transductive inference. The given data are thought of as a graph, where the edges encode the pairwise relationships among data. We develop discrete analysis and geometry on graphs, and then naturally adapt the classical regularization in the continuous case to the graph situation. A new and effective algorithm is derived from this general framework, as well as an approach we developed before.

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Zhou, D., Schölkopf, B. Transductive Inference with Graphs Max Planck Institute for Biological Cybernetics, 2004, See the improved version Regularization on Discrete Spaces. (techreport)

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Classification and Feature Extraction in Man and Machine

Graf, AAB.

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

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Graf, AAB. Classification and Feature Extraction in Man and Machine Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

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Kompetenzerwerb für Informationssysteme - Einfluss des Lernprozesses auf die Interaktion mit Fahrerinformationssystemen. Veröffentlichter Abschlussbericht (Förderkennzeichen BaSt FE 82.196/2001).

Totzke, I., Krüger, H., Hofmann, M., Meilinger, T., Rauch, N., Schmidt, G.

Interdisziplinäres Zentrum für Verkehrswissenschaften (IZVW), Würzburg, 2004 (techreport)

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Totzke, I., Krüger, H., Hofmann, M., Meilinger, T., Rauch, N., Schmidt, G. Kompetenzerwerb für Informationssysteme - Einfluss des Lernprozesses auf die Interaktion mit Fahrerinformationssystemen. Veröffentlichter Abschlussbericht (Förderkennzeichen BaSt FE 82.196/2001). Interdisziplinäres Zentrum für Verkehrswissenschaften (IZVW), Würzburg, 2004 (techreport)

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2002

Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.

(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

Abstract

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.

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2002

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V. Kernel Dependency Estimation (98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

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Global Geometry of SVM Classifiers

Zhou, D., Xiao, B., Zhou, H., Dai, R.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

Abstract

We construct an geometry framework for any norm Support Vector Machine (SVM) classifiers. Within this framework, separating hyperplanes, dual descriptions and solutions of SVM classifiers are constructed by a purely geometric fashion. In contrast with the optimization theory used in SVM classifiers, we have no complicated computations any more. Each step in our theory is guided by elegant geometric intuitions.

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Zhou, D., Xiao, B., Zhou, H., Dai, R. Global Geometry of SVM Classifiers Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

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Computationally Efficient Face Detection

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

(MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

Web [BibTex]

Romdhani, S., Torr, P., Schölkopf, B., Blake, A. Computationally Efficient Face Detection (MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

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Nonlinear Multivariate Analysis with Geodesic Kernels

Kuss, M.

Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

GZIP [BibTex]

Kuss, M. Nonlinear Multivariate Analysis with Geodesic Kernels Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

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Kernel-based nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

EU-Project BLISS, January 2002 (techreport)

GZIP [BibTex]

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K. Kernel-based nonlinear blind source separation EU-Project BLISS, January 2002 (techreport)

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Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms

Bousquet, O.

Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

Abstract

New classification algorithms based on the notion of 'margin' (e.g. Support Vector Machines, Boosting) have recently been developed. The goal of this thesis is to better understand how they work, via a study of their theoretical performance. In order to do this, a general framework for real-valued classification is proposed. In this framework, it appears that the natural tools to use are Concentration Inequalities and Empirical Processes Theory. Thanks to an adaptation of these tools, a new measure of the size of a class of functions is introduced, which can be computed from the data. This allows, on the one hand, to better understand the role of eigenvalues of the kernel matrix in Support Vector Machines, and on the other hand, to obtain empirical model selection criteria.

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Bousquet, O. Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

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Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Chapelle, O.

Biologische Kybernetik, 2002 (phdthesis)

Abstract

This thesis presents a theoretical and practical study of Support Vector Machines (SVM) and related learning algorithms. In a first part, we introduce a new induction principle from which SVMs can be derived, but some new algorithms are also presented in this framework. In a second part, after studying how to estimate the generalization error of an SVM, we suggest to choose the kernel parameters of an SVM by minimizing this estimate. Several applications such as feature selection are presented. Finally the third part deals with the incoporation of prior knowledge in a learning algorithm and more specifically, we studied the case of known invariant transormations and the use of unlabeled data.

GZIP [BibTex]

Chapelle, O. Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge Biologische Kybernetik, 2002 (phdthesis)

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A compression approach to support vector model selection

von Luxburg, U., Bousquet, O., Schölkopf, B.

(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

Abstract

In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct ``compression coefficients'' for SVMs, which measure the amount by which the training labels can be compressed by some classification hypothesis. The main idea is to relate the coding precision of this hypothesis to the width of the margin of the SVM. The compression coefficients connect well known quantities such as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel matrix and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on several real world datasets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.

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von Luxburg, U., Bousquet, O., Schölkopf, B. A compression approach to support vector model selection (101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

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Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Observations on the Nyström Method for Gaussian Process Prediction

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

Abstract

A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nystr{\"o}m method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nystr{\"o}m method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nystr{\"o}m approximation would be expected to provide a good approximation to exact GP regression.

PostScript [BibTex]

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V. Observations on the Nyström Method for Gaussian Process Prediction Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

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2001

Kernel Methods for Extracting Local Image Semantics

Bradshaw, B., Schölkopf, B., Platt, J.

(MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Web [BibTex]

2001

Bradshaw, B., Schölkopf, B., Platt, J. Kernel Methods for Extracting Local Image Semantics (MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Web [BibTex]

Calibration of Digital Amateur Cameras

Urbanek, M., Horaud, R., Sturm, P.

(RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]

Urbanek, M., Horaud, R., Sturm, P. Calibration of Digital Amateur Cameras (RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]

Variationsverfahren zur Untersuchung von Grundzustandseigenschaften des Ein-Band Hubbard-Modells

Eichhorn, J.

Biologische Kybernetik, Technische Universität Dresden, Dresden/Germany, May 2001 (diplomathesis)

Abstract

Using different modifications of a new variational approach, statical groundstate properties of the one-band Hubbard model such as energy and staggered magnetisation are calculated. By taking into account additional fluctuations, the method ist gradually improved so that a very good description of the energy in one and two dimensions can be achieved. After a detailed discussion of the application in one dimension, extensions for two dimensions are introduced. By use of a modified version of the variational ansatz in particular a description of the quantum phase transition for the magnetisation should be possible.

PostScript [BibTex]

Eichhorn, J. Variationsverfahren zur Untersuchung von Grundzustandseigenschaften des Ein-Band Hubbard-Modells Biologische Kybernetik, Technische Universität Dresden, Dresden/Germany, May 2001 (diplomathesis)

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Use of the ell_0-norm with linear models and kernel methods

Weston, J., Elisseeff, A., Schölkopf, B.

Biowulf Technologies, 2001 (techreport)

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Weston, J., Elisseeff, A., Schölkopf, B. Use of the ell_0-norm with linear models and kernel methods Biowulf Technologies, 2001 (techreport)

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KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

BIOwulf, 2001 (techreport)

Web [BibTex]

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin BIOwulf, 2001 (techreport)

Web [BibTex]

Cerebellar Control of Robot Arms

Peters, J.

Biologische Kybernetik, Technische Univeristät München, München, Germany, 2001 (diplomathesis)

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Peters, J. Cerebellar Control of Robot Arms Biologische Kybernetik, Technische Univeristät München, München, Germany, 2001 (diplomathesis)

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Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O., Schölkopf, B.

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

Abstract

We consider the problem of how to incorporate in the Support Vector Machine (SVM) framework invariances given by some a priori known transformations under which the data should be invariant. It extends some previous work which was only applicable with linear SVMs and we show on a digit recognition task that the proposed approach is superior to the traditional Virtual Support Vector method.

PostScript [BibTex]

Chapelle, O., Schölkopf, B. Incorporating Invariances in Non-Linear Support Vector Machines Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

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Data cleaning algorithms with applications to micro-array experiments

Weston, J., Chapelle, O., Guyon, I.

Biowulf, 2001 (techreport)

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Weston, J., Chapelle, O., Guyon, I. Data cleaning algorithms with applications to micro-array experiments Biowulf, 2001 (techreport)

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On Unsupervised Learning of Mixtures of Markov Sources

Seldin, Y.

Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, 2001 (diplomathesis)

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Seldin, Y. On Unsupervised Learning of Mixtures of Markov Sources Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, 2001 (diplomathesis)

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Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs

Gretton, A., Herbrich, R., Schölkopf, B., Smola, A., Rayner, P.

University of Cambridge, 2001 (techreport)

[BibTex]

Gretton, A., Herbrich, R., Schölkopf, B., Smola, A., Rayner, P. Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs University of Cambridge, 2001 (techreport)

[BibTex]

Bound on the Leave-One-Out Error for 2-Class Classification using nu-SVMs

Gretton, A., Herbrich, R., Schölkopf, B., Rayner, P.

University of Cambridge, 2001, Updated May 2003 (literature review expanded) (techreport)

Abstract

Three estimates of the leave-one-out error for $nu$-support vector (SV) machine binary classifiers are presented. Two of the estimates are based on the geometrical concept of the {em span}, which was introduced in the context of bounding the leave-one-out error for $C$-SV machine binary classifiers, while the third is based on optimisation over the criterion used to train the $nu$-support vector classifier. It is shown that the estimates presented herein provide informative and efficient approximations of the generalisation behaviour, in both a toy example and benchmark data sets. The proof strategies in the $nu$-SV context are also compared with those used to derive leave-one-out error estimates in the $C$-SV case.

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