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145 results (View BibTeX file of all listed publications)

2005

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Phenotypic characterization of chondrosarcoma-derived cell lines

Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T.

Cancer Letters, 226(2):143-154, August 2005 (article)

Abstract
Gene expression profiling of three chondrosarcoma derived cell lines (AD, SM, 105KC) showed an increased proliferative activity and a reduced expression of chondrocytic-typical matrix products compared to primary chondrocytes. The incapability to maintain an adequate matrix synthesis as well as a notable proliferative activity at the same time is comparable to neoplastic chondrosarcoma cells in vivo which cease largely cartilage matrix formation as soon as their proliferative activity increases. Thus, the investigated cell lines are of limited value as substitute of primary chondrocytes but might have a much higher potential to investigate the behavior of neoplastic chondrocytes, i.e. chondrosarcoma biology.

Web [BibTex]

2005

Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T. Phenotypic characterization of chondrosarcoma-derived cell lines Cancer Letters, 226(2):143-154, August 2005 (article)

Web [BibTex]

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Local Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

Abstract
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

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Bartlett, P., Bousquet, O., Mendelson, S. Local Rademacher Complexities The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

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Inlier-based ICA with an application to superimposed images

Meinecke, F., Harmeling, S., Müller, K.

International Journal of Imaging Systems and Technology, 15(1):48-55, July 2005 (article)

Abstract
This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals).

Web DOI [BibTex]

Meinecke, F., Harmeling, S., Müller, K. Inlier-based ICA with an application to superimposed images International Journal of Imaging Systems and Technology, 15(1):48-55, July 2005 (article)

Web DOI [BibTex]

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Learning the Kernel with Hyperkernels

Ong, CS., Smola, A., Williamson, R.

Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

Abstract
This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.

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Ong, CS., Smola, A., Williamson, R. Learning the Kernel with Hyperkernels Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

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Image Reconstruction by Linear Programming

Tsuda, K., Rätsch, G.

IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)

Abstract
One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion. We propose a new method to identify the noisy pixels by l1-norm penalization and to update the identified pixels only. The identification and updating of noisy pixels are formulated as one linear program which can be efficiently solved. In particular, one can apply the upsilon trick to directly specify the fraction of pixels to be reconstructed. Moreover, we extend the linear program to be able to exploit prior knowledge that occlusions often appear in contiguous blocks (e.g., sunglasses on faces). The basic idea is to penalize boundary points and interior points of the occluded area differently. We are also able to show the upsilon property for this extended LP leading to a method which is easy to use. Experimental results demonstrate the power of our approach.

PDF DOI [BibTex]

Tsuda, K., Rätsch, G. Image Reconstruction by Linear Programming IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)

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RASE: recognition of alternatively spliced exons in C.elegans

Rätsch, G., Sonnenburg, S., Schölkopf, B.

Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)

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Rätsch, G., Sonnenburg, S., Schölkopf, B. RASE: recognition of alternatively spliced exons in C.elegans Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)

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Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Tsuda, K., Rätsch, G., Warmuth, M.

Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

Abstract
We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the exponentiated gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the derivation and the analyses of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the resulting matrix exponentiated gradient (MEG) update and DefiniteBoost to the problem of learning a kernel matrix from distance measurements.

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Tsuda, K., Rätsch, G., Warmuth, M. Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

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Protein function prediction via graph kernels

Borgwardt, KM., Ong, CS., Schönauer, S., Vishwanathan, ., Smola, AJ., Kriegel, H-P.

Bioinformatics, 21(Suppl. 1: ISMB 2005 Proceedings):i47-i56, June 2005 (article)

Abstract
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.

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Borgwardt, KM., Ong, CS., Schönauer, S., Vishwanathan, ., Smola, AJ., Kriegel, H-P. Protein function prediction via graph kernels Bioinformatics, 21(Suppl. 1: ISMB 2005 Proceedings):i47-i56, June 2005 (article)

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Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Journal of the Optical Society of America A, 22(5):801-809, May 2005 (article)

Abstract
A number of models of depth cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum variance unbiased estimator that can be constructed from the available information. Here we test such models using visual and haptic depth information. Different texture types produce differences in slant discrimination performance, providing a means for testing a reliability-sensitive cue combination model using texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability, but fell short of statistically optimal combination—we find reliability-based re-weighting, but not statistically optimal cue combination.

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Rosas, P., Wagemans, J., Ernst, M., Wichmann, F. Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination Journal of the Optical Society of America A, 22(5):801-809, May 2005 (article)

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Bayesian inference for psychometric functions

Kuss, M., Jäkel, F., Wichmann, F.

Journal of Vision, 5(5):478-492, May 2005 (article)

Abstract
In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer’s ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

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A gene expression map of Arabidopsis thaliana development

Schmid, M., Davison, T., Henz, S., Pape, U., Demar, M., Vingron, M., Schölkopf, B., Weigel, D., Lohmann, J.

Nature Genetics, 37(5):501-506, April 2005 (article)

Abstract
Regulatory regions of plant genes tend to be more compact than those of animal genes, but the complement of transcription factors encoded in plant genomes is as large or larger than that found in those of animals. Plants therefore provide an opportunity to study how transcriptional programs control multicellular development. We analyzed global gene expression during development of the reference plant Arabidopsis thaliana in samples covering many stages, from embryogenesis to senescence, and diverse organs. Here, we provide a first analysis of this data set, which is part of the AtGenExpress expression atlas. We observed that the expression levels of transcription factor genes and signal transduction components are similar to those of metabolic genes. Examining the expression patterns of large gene families, we found that they are often more similar than would be expected by chance, indicating that many gene families have been co-opted for specific developmental processes.

PDF DOI [BibTex]

Schmid, M., Davison, T., Henz, S., Pape, U., Demar, M., Vingron, M., Schölkopf, B., Weigel, D., Lohmann, J. A gene expression map of Arabidopsis thaliana development Nature Genetics, 37(5):501-506, April 2005 (article)

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Morphological characterization of molecular complexes present in the synaptic cleft

Lucic, V., Yang, T., Schweikert, G., Förster, F., Baumeister, W.

Structure, 13(3):423-434, March 2005 (article)

Abstract
We obtained tomograms of isolated mammalian excitatory synapses by cryo-electron tomography. This method allows the investigation of biological material in the frozen-hydrated state, without staining, and can therefore provide reliable structural information at the molecular level. We developed an automated procedure for the segmentation of molecular complexes present in the synaptic cleft based on thresholding and connectivity, and calculated several morphological characteristics of these complexes. Extensive lateral connections along the synaptic cleft are shown to form a highly connected structure with a complex topology. Our results are essentially parameter-free, i.e., they do not depend on the choice of certain parameter values (such as threshold). In addition, the results are not sensitive to noise; the same conclusions can be drawn from the analysis of both nondenoised and denoised tomograms.

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Lucic, V., Yang, T., Schweikert, G., Förster, F., Baumeister, W. Morphological characterization of molecular complexes present in the synaptic cleft Structure, 13(3):423-434, March 2005 (article)

PDF DOI [BibTex]

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Experimentally optimal v in support vector regression for different noise models and parameter settings

Chalimourda, A., Schölkopf, B., Smola, A.

Neural Networks, 18(2):205-205, March 2005 (article)

PDF DOI [BibTex]

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Semi-supervised protein classification using cluster kernels

Weston, J., Leslie, C., Ie, E., Zhou, D., Elisseeff, A., Noble, W.

Bioinformatics, 21(15):3241-3247, 2005 (article)

[BibTex]

Weston, J., Leslie, C., Ie, E., Zhou, D., Elisseeff, A., Noble, W. Semi-supervised protein classification using cluster kernels Bioinformatics, 21(15):3241-3247, 2005 (article)

[BibTex]

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Invariance of Neighborhood Relation under Input Space to Feature Space Mapping

Shin, H., Cho, S.

Pattern Recognition Letters, 26(6):707-718, 2005 (article)

Abstract
If the training pattern set is large, it takes a large memory and a long time to train support vector machine (SVM). Recently, we proposed neighborhood property based pattern selection algorithm (NPPS) which selects only the patterns that are likely to be near the decision boundary ahead of SVM training [Proc. of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Lecture Notes in Artificial Intelligence (LNAI 2637), Seoul, Korea, pp. 376–387]. NPPS tries to identify those patterns that are likely to become support vectors in feature space. Preliminary reports show its effectiveness: SVM training time was reduced by two orders of magnitude with almost no loss in accuracy for various datasets. It has to be noted, however, that decision boundary of SVM and support vectors are all defined in feature space while NPPS described above operates in input space. If neighborhood relation in input space is not preserved in feature space, NPPS may not always be effective. In this paper, we sh ow that the neighborhood relation is invariant under input to feature space mapping. The result assures that the patterns selected by NPPS in input space are likely to be located near decision boundary in feature space.

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Shin, H., Cho, S. Invariance of Neighborhood Relation under Input Space to Feature Space Mapping Pattern Recognition Letters, 26(6):707-718, 2005 (article)

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Graph Kernels for Chemical Informatics

Ralaivola, L., Swamidass, J., Saigo, H., Baldi, P.

Neural Networks, 18(8):1093-1110, 2005 (article)

Abstract
Increased availability of large repositories of chemical compounds is creating new challenges and opportunities for the application of machine learning methods to problems in computational chemistry and chemical informatics. Because chemical compounds are often represented by the graph of their covalent bonds, machine learning methods in this domain must be capable of processing graphical structures with variable size. Here we first briefly review the literature on graph kernels and then introduce three new kernels (Tanimoto, MinMax, Hybrid) based on the idea of molecular fingerprints and counting labeled paths of depth up to d using depthfirst search from each possible vertex. The kernels are applied to three classification problems to predict mutagenicity, toxicity, and anti-cancer activity on three publicly available data sets. The kernels achieve performances at least comparable, and most often superior, to those previously reported in the literature reaching accuracies of 91.5% on the Mutag dataset, 65-67% on the PTC (Predictive Toxicology Challenge) dataset, and 72% on the NCI (National Cancer Institute) dataset. Properties and tradeoffs of these kernels, as well as other proposed kernels that leverage 1D or 3D representations of molecules, are briefly discussed.

PDF DOI [BibTex]

Ralaivola, L., Swamidass, J., Saigo, H., Baldi, P. Graph Kernels for Chemical Informatics Neural Networks, 18(8):1093-1110, 2005 (article)

PDF DOI [BibTex]

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Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures

Zhang, K., Chan, L.

Neural Computation, 17(2):425-452, 2005 (article)

Abstract
The linear mixture model has been investigated in most articles tackling the problem of blind source separation. Recently, several articles have addressed a more complex model: blind source separation (BSS) of post-nonlinear (PNL) mixtures. These mixtures are assumed to be generated by applying an unknown invertible nonlinear distortion to linear instantaneous mixtures of some independent sources. The gaussianization technique for BSS of PNL mixtures emerged based on the assumption that the distribution of the linear mixture of independent sources is gaussian. In this letter, we review the gaussianization method and then extend it to apply to PNL mixture in which the linear mixture is close to gaussian. Our proposed method approximates the linear mixture using the Cornish-Fisher expansion. We choose the mutual information as the independence measurement to develop a learning algorithm to separate PNL mixtures. This method provides better applicability and accuracy. We then discuss the sufficient condition for the method to be valid. The characteristics of the nonlinearity do not affect the performance of this method. With only a few parameters to tune, our algorithm has a comparatively low computation. Finally, we present experiments to illustrate the efficiency of our method.

Web DOI [BibTex]

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Theory of Classification: A Survey of Some Recent Advances

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

ESAIM: Probability and Statistics, 9, pages: 323 , 2005 (article)

Abstract
The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have lead to these important recent developments.

PDF DOI [BibTex]

Boucheron, S., Bousquet, O., Lugosi, G. Theory of Classification: A Survey of Some Recent Advances ESAIM: Probability and Statistics, 9, pages: 323 , 2005 (article)

PDF DOI [BibTex]

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Moment Inequalities for Functions of Independent Random Variables

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

To appear in Annals of Probability, 33, pages: 514-560, 2005 (article)

Abstract
A general method for obtaining moment inequalities for functions of independent random variables is presented. It is a generalization of the entropy method which has been used to derive concentration inequalities for such functions cite{BoLuMa01}, and is based on a generalized tensorization inequality due to Lata{l}a and Oleszkiewicz cite{LaOl00}. The new inequalities prove to be a versatile tool in a wide range of applications. We illustrate the power of the method by showing how it can be used to effortlessly re-derive classical inequalities including Rosenthal and Kahane-Khinchine-type inequalities for sums of independent random variables, moment inequalities for suprema of empirical processes, and moment inequalities for Rademacher chaos and $U$-statistics. Some of these corollaries are apparently new. In particular, we generalize Talagrands exponential inequality for Rademacher chaos of order two to any order. We also discuss applications for other complex functions of independent random variables, such as suprema of boolean polynomials which include, as special cases, subgraph counting problems in random graphs.

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Boucheron, S., Bousquet, O., Lugosi, G., Massart, P. Moment Inequalities for Functions of Independent Random Variables To appear in Annals of Probability, 33, pages: 514-560, 2005 (article)

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A novel representation of protein sequences for prediction of subcellular location using support vector machines

Matsuda, S., Vert, J., Saigo, H., Ueda, N., Toh, H., Akutsu, T.

Protein Science, 14, pages: 2804-2813, 2005 (article)

Abstract
As the number of complete genomes rapidly increases, accurate methods to automatically predict the subcellular location of proteins are increasingly useful to help their functional annotation. In order to improve the predictive accuracy of the many prediction methods developed to date, a novel representation of protein sequences is proposed. This representation involves local compositions of amino acids and twin amino acids, and local frequencies of distance between successive (basic, hydrophobic, and other) amino acids. For calculating the local features, each sequence is split into three parts: N-terminal, middle, and C-terminal. The N-terminal part is further divided into four regions to consider ambiguity in the length and position of signal sequences. We tested this representation with support vector machines on two data sets extracted from the SWISS-PROT database. Through fivefold cross-validation tests, overall accuracies of more than 87% and 91% were obtained for eukaryotic and prokaryotic proteins, respectively. It is concluded that considering the respective features in the N-terminal, middle, and C-terminal parts is helpful to predict the subcellular location. Keywords: subcellular location; signal sequence; amino acid composition; distance frequency; support vector machine; predictive accuracy

Web DOI [BibTex]

Matsuda, S., Vert, J., Saigo, H., Ueda, N., Toh, H., Akutsu, T. A novel representation of protein sequences for prediction of subcellular location using support vector machines Protein Science, 14, pages: 2804-2813, 2005 (article)

Web DOI [BibTex]

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A tutorial on v-support vector machines

Chen, P., Lin, C., Schölkopf, B.

Applied Stochastic Models in Business and Industry, 21(2):111-136, 2005 (article)

Abstract
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.

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Chen, P., Lin, C., Schölkopf, B. A tutorial on v-support vector machines Applied Stochastic Models in Business and Industry, 21(2):111-136, 2005 (article)

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Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces

Schröder, M., Lal, T., Hinterberger, T., Bogdan, M., Hill, J., Birbaumer, N., Rosenstiel, W., Schölkopf, B.

EURASIP Journal on Applied Signal Processing, 2005(19, Special Issue: Trends in Brain Computer Interfaces):3103-3112, (Editors: Vesin, J. M., T. Ebrahimi), 2005 (article)

Abstract
Most EEG-based Brain Computer Interface (BCI) paradigms come along with specific electrode positions, e.g.~for a visual based BCI electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects Lal et.~al showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extend their method of Recursive Channel Elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

Web DOI [BibTex]

Schröder, M., Lal, T., Hinterberger, T., Bogdan, M., Hill, J., Birbaumer, N., Rosenstiel, W., Schölkopf, B. Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces EURASIP Journal on Applied Signal Processing, 2005(19, Special Issue: Trends in Brain Computer Interfaces):3103-3112, (Editors: Vesin, J. M., T. Ebrahimi), 2005 (article)

Web DOI [BibTex]

2002

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Optimized Support Vector Machines for Nonstationary Signal Classification

Davy, M., Gretton, A., Doucet, A., Rayner, P.

IEEE Signal Processing Letters, 9(12):442-445, December 2002 (article)

Abstract
This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimised in a principled way. Simulations demonstrate that our low complexity method outperforms state-of-the-art nonstationary signal classification techniques.

PostScript Web DOI [BibTex]

2002

Davy, M., Gretton, A., Doucet, A., Rayner, P. Optimized Support Vector Machines for Nonstationary Signal Classification IEEE Signal Processing Letters, 9(12):442-445, December 2002 (article)

PostScript Web DOI [BibTex]

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A New Discriminative Kernel from Probabilistic Models

Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.

Neural Computation, 14(10):2397-2414, October 2002 (article)

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Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K. A New Discriminative Kernel from Probabilistic Models Neural Computation, 14(10):2397-2414, October 2002 (article)

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Functional Genomics of Osteoarthritis

Aigner, T., Bartnik, E., Zien, A., Zimmer, R.

Pharmacogenomics, 3(5):635-650, September 2002 (article)

Web [BibTex]

Aigner, T., Bartnik, E., Zien, A., Zimmer, R. Functional Genomics of Osteoarthritis Pharmacogenomics, 3(5):635-650, September 2002 (article)

Web [BibTex]

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Constructing Boosting algorithms from SVMs: an application to one-class classification.

Rätsch, G., Mika, S., Schölkopf, B., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, September 2002 (article)

Abstract
We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm—one-class leveraging—starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

DOI [BibTex]

Rätsch, G., Mika, S., Schölkopf, B., Müller, K. Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, September 2002 (article)

DOI [BibTex]

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Co-Clustering of Biological Networks and Gene Expression Data

Hanisch, D., Zien, A., Zimmer, R., Lengauer, T.

Bioinformatics, (Suppl 1):145S-154S, 18, July 2002 (article)

Abstract
Motivation: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. Results: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

Web [BibTex]

Hanisch, D., Zien, A., Zimmer, R., Lengauer, T. Co-Clustering of Biological Networks and Gene Expression Data Bioinformatics, (Suppl 1):145S-154S, 18, July 2002 (article)

Web [BibTex]

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Confidence measures for protein fold recognition

Sommer, I., Zien, A., von Ohsen, N., Zimmer, R., Lengauer, T.

Bioinformatics, 18(6):802-812, June 2002 (article)

[BibTex]

Sommer, I., Zien, A., von Ohsen, N., Zimmer, R., Lengauer, T. Confidence measures for protein fold recognition Bioinformatics, 18(6):802-812, June 2002 (article)

[BibTex]

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The contributions of color to recognition memory for natural scenes

Wichmann, F., Sharpe, L., Gegenfurtner, K.

Journal of Experimental Psychology: Learning, Memory and Cognition, 28(3):509-520, May 2002 (article)

Abstract
The authors used a recognition memory paradigm to assess the influence of color information on visual memory for images of natural scenes. Subjects performed 5-10% better for colored than for black-and-white images independent of exposure duration. Experiment 2 indicated little influence of contrast once the images were suprathreshold, and Experiment 3 revealed that performance worsened when images were presented in color and tested in black and white, or vice versa, leading to the conclusion that the surface property color is part of the memory representation. Experiments 4 and 5 exclude the possibility that the superior recognition memory for colored images results solely from attentional factors or saliency. Finally, the recognition memory advantage disappears for falsely colored images of natural scenes: The improvement in recognition memory depends on the color congruence of presented images with learned knowledge about the color gamut found within natural scenes. The results can be accounted for within a multiple memory systems framework.

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Wichmann, F., Sharpe, L., Gegenfurtner, K. The contributions of color to recognition memory for natural scenes Journal of Experimental Psychology: Learning, Memory and Cognition, 28(3):509-520, May 2002 (article)

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Training invariant support vector machines

DeCoste, D., Schölkopf, B.

Machine Learning, 46(1-3):161-190, January 2002 (article)

Abstract
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.

PDF DOI [BibTex]

DeCoste, D., Schölkopf, B. Training invariant support vector machines Machine Learning, 46(1-3):161-190, January 2002 (article)

PDF DOI [BibTex]

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Model Selection for Small Sample Regression

Chapelle, O., Vapnik, V., Bengio, Y.

Machine Learning, 48(1-3):9-23, 2002 (article)

Abstract
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitting and underfitting. Previous classical results for linear regression are based on an asymptotic analysis. We present a new penalization method for performing model selection for regression that is appropriate even for small samples. Our penalization is based on an accurate estimator of the ratio of the expected training error and the expected generalization error, in terms of the expected eigenvalues of the input covariance matrix.

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Chapelle, O., Vapnik, V., Bengio, Y. Model Selection for Small Sample Regression Machine Learning, 48(1-3):9-23, 2002 (article)

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Contrast discrimination with sinusoidal gratings of different spatial frequency

Bird, C., Henning, G., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1267-1273, 2002 (article)

Abstract
The detectability of contrast increments was measured as a function of the contrast of a masking or “pedestal” grating at a number of different spatial frequencies ranging from 2 to 16 cycles per degree of visual angle. The pedestal grating always had the same orientation, spatial frequency and phase as the signal. The shape of the contrast increment threshold versus pedestal contrast (TvC) functions depend of the performance level used to define the “threshold,” but when both axes are normalized by the contrast corresponding to 75% correct detection at each frequency, the (TvC) functions at a given performance level are identical. Confidence intervals on the slope of the rising part of the TvC functions are so wide that it is not possible with our data to reject Weber’s Law.

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Bird, C., Henning, G., Wichmann, F. Contrast discrimination with sinusoidal gratings of different spatial frequency Journal of the Optical Society of America A, 19(7), pages: 1267-1273, 2002 (article)

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A Bennett Concentration Inequality and Its Application to Suprema of Empirical Processes

Bousquet, O.

C. R. Acad. Sci. Paris, Ser. I, 334, pages: 495-500, 2002 (article)

Abstract
We introduce new concentration inequalities for functions on product spaces. They allow to obtain a Bennett type deviation bound for suprema of empirical processes indexed by upper bounded functions. The result is an improvement on Rio's version \cite{Rio01b} of Talagrand's inequality \cite{Talagrand96} for equidistributed variables.

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Numerical evolution of axisymmetric, isolated systems in general relativity

Frauendiener, J., Hein, M.

Physical Review D, 66, pages: 124004-124004, 2002 (article)

Abstract
We describe in this article a new code for evolving axisymmetric isolated systems in general relativity. Such systems are described by asymptotically flat space-times, which have the property that they admit a conformal extension. We are working directly in the extended conformal manifold and solve numerically Friedrich's conformal field equations, which state that Einstein's equations hold in the physical space-time. Because of the compactness of the conformal space-time the entire space-time can be calculated on a finite numerical grid. We describe in detail the numerical scheme, especially the treatment of the axisymmetry and the boundary.

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Frauendiener, J., Hein, M. Numerical evolution of axisymmetric, isolated systems in general relativity Physical Review D, 66, pages: 124004-124004, 2002 (article)

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Marginalized kernels for biological sequences

Tsuda, K., Kin, T., Asai, K.

Bioinformatics, 18(Suppl 1):268-275, 2002 (article)

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Tsuda, K., Kin, T., Asai, K. Marginalized kernels for biological sequences Bioinformatics, 18(Suppl 1):268-275, 2002 (article)

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Stability and Generalization

Bousquet, O., Elisseeff, A.

Journal of Machine Learning Research, 2, pages: 499-526, 2002 (article)

Abstract
We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.

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Bousquet, O., Elisseeff, A. Stability and Generalization Journal of Machine Learning Research, 2, pages: 499-526, 2002 (article)

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Subspace information criterion for non-quadratic regularizers – model selection for sparse regressors

Tsuda, K., Sugiyama, M., Müller, K.

IEEE Trans Neural Networks, 13(1):70-80, 2002 (article)

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Tsuda, K., Sugiyama, M., Müller, K. Subspace information criterion for non-quadratic regularizers – model selection for sparse regressors IEEE Trans Neural Networks, 13(1):70-80, 2002 (article)

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Modeling splicing sites with pairwise correlations

Arita, M., Tsuda, K., Asai, K.

Bioinformatics, 18(Suppl 2):27-34, 2002 (article)

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Arita, M., Tsuda, K., Asai, K. Modeling splicing sites with pairwise correlations Bioinformatics, 18(Suppl 2):27-34, 2002 (article)

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Perfusion Quantification using Gaussian Process Deconvolution

Andersen, IK., Szymkowiak, A., Rasmussen, CE., Hanson, LG., Marstrand, JR., Larsson, HBW., Hansen, LK.

Magnetic Resonance in Medicine, (48):351-361, 2002 (article)

Abstract
The quantification of perfusion using dynamic susceptibility contrast MR imaging requires deconvolution to obtain the residual impulse-response function (IRF). Here, a method using a Gaussian process for deconvolution, GPD, is proposed. The fact that the IRF is smooth is incorporated as a constraint in the method. The GPD method, which automatically estimates the noise level in each voxel, has the advantage that model parameters are optimized automatically. The GPD is compared to singular value decomposition (SVD) using a common threshold for the singular values and to SVD using a threshold optimized according to the noise level in each voxel. The comparison is carried out using artificial data as well as using data from healthy volunteers. It is shown that GPD is comparable to SVD variable optimized threshold when determining the maximum of the IRF, which is directly related to the perfusion. GPD provides a better estimate of the entire IRF. As the signal to noise ratio increases or the time resolution of the measurements increases, GPD is shown to be superior to SVD. This is also found for large distribution volumes.

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Andersen, IK., Szymkowiak, A., Rasmussen, CE., Hanson, LG., Marstrand, JR., Larsson, HBW., Hansen, LK. Perfusion Quantification using Gaussian Process Deconvolution Magnetic Resonance in Medicine, (48):351-361, 2002 (article)

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Tracking a Small Set of Experts by Mixing Past Posteriors

Bousquet, O., Warmuth, M.

Journal of Machine Learning Research, 3, pages: 363-396, (Editors: Long, P.), 2002 (article)

Abstract
In this paper, we examine on-line learning problems in which the target concept is allowed to change over time. In each trial a master algorithm receives predictions from a large set of n experts. Its goal is to predict almost as well as the best sequence of such experts chosen off-line by partitioning the training sequence into k+1 sections and then choosing the best expert for each section. We build on methods developed by Herbster and Warmuth and consider an open problem posed by Freund where the experts in the best partition are from a small pool of size m. Since k >> m, the best expert shifts back and forth between the experts of the small pool. We propose algorithms that solve this open problem by mixing the past posteriors maintained by the master algorithm. We relate the number of bits needed for encoding the best partition to the loss bounds of the algorithms. Instead of paying log n for choosing the best expert in each section we first pay log (n choose m) bits in the bounds for identifying the pool of m experts and then log m bits per new section. In the bounds we also pay twice for encoding the boundaries of the sections.

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Bousquet, O., Warmuth, M. Tracking a Small Set of Experts by Mixing Past Posteriors Journal of Machine Learning Research, 3, pages: 363-396, (Editors: Long, P.), 2002 (article)

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A femoral arteriovenous shunt facilitates arterial whole blood sampling in animals

Weber, B., Burger, C., Biro, P., Buck, A.

Eur J Nucl Med Mol Imaging, 29, pages: 319-323, 2002 (article)

[BibTex]

Weber, B., Burger, C., Biro, P., Buck, A. A femoral arteriovenous shunt facilitates arterial whole blood sampling in animals Eur J Nucl Med Mol Imaging, 29, pages: 319-323, 2002 (article)

[BibTex]

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Contrast discrimination with pulse-trains in pink noise

Henning, G., Bird, C., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

Abstract
Detection performance was measured with sinusoidal and pulse-train gratings. Although the 2.09-c/deg pulse-train, or line gratings, contained at least 8 harmonics all at equal contrast, they were no more detectable than their most detectable component. The addition of broadband pink noise designed to equalize the detectability of the components of the pulse train made the pulse train about a factor of four more detectable than any of its components. However, in contrast-discrimination experiments, with a pedestal or masking grating of the same form and phase as the signal and 15% contrast, the noise did not affect the discrimination performance of the pulse train relative to that obtained with its sinusoidal components. We discuss the implications of these observations for models of early vision in particular the implications for possible sources of internal noise.

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Henning, G., Bird, C., Wichmann, F. Contrast discrimination with pulse-trains in pink noise Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

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Choosing Multiple Parameters for Support Vector Machines

Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.

Machine Learning, 46(1):131-159, 2002 (article)

Abstract
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVM) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.

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Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S. Choosing Multiple Parameters for Support Vector Machines Machine Learning, 46(1):131-159, 2002 (article)

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