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2004


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On the representation, learning and transfer of spatio-temporal movement characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, M.

International Journal of Humanoid Robotics, 1(4):613-636, December 2004 (article)

[BibTex]

2004

[BibTex]


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Insect-inspired estimation of egomotion

Franz, MO., Chahl, JS., Krapp, HG.

Neural Computation, 16(11):2245-2260, November 2004 (article)

Abstract
Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

PDF PostScript Web DOI [BibTex]

PDF PostScript Web DOI [BibTex]


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Efficient face detection by a cascaded support-vector machine expansion

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

Proceedings of The Royal Society of London A, 460(2501):3283-3297, A, November 2004 (article)

Abstract
We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Advanced Lectures on Machine Learning

Bousquet, O., von Luxburg, U., Rätsch, G.

ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Abstract
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in T{\"u}bingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Web [BibTex]

Web [BibTex]


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Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175

Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B.

Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Learning kernels from biological networks by maximizing entropy

Tsuda, K., Noble, W.

Bioinformatics, 20(Suppl. 1):i326-i333, August 2004 (article)

Abstract
Motivation: The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. Results: We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein–protein interaction networks.

PDF Web [BibTex]

PDF Web [BibTex]


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Masking effect produced by Mach bands on the detection of narrow bars of random polarity

Henning, GB., Hoddinott, KT., Wilson-Smith, ZJ., Hill, NJ.

Journal of the Optical Society of America, 21(8):1379-1387, A, August 2004 (article)

[BibTex]

[BibTex]


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Support Vector Channel Selection in BCI

Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.

IEEE Transactions on Biomedical Engineering, 51(6):1003-1010, June 2004 (article)

Abstract
Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying EEG signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of Support Vector Machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.

DOI [BibTex]

DOI [BibTex]


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Distance-Based Classification with Lipschitz Functions

von Luxburg, U., Bousquet, O.

Journal of Machine Learning Research, 5, pages: 669-695, June 2004 (article)

Abstract
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. It will turn out that using Lipschitz functions as decision functions, the inverse of the Lipschitz constant can be interpreted as the size of a margin. In order to construct a clean mathematical setup we isometrically embed the given metric space into a Banach space and the space of Lipschitz functions into its dual space. To analyze the resulting algorithm, we prove several representer theorems. They state that there always exist solutions of the Lipschitz classifier which can be expressed in terms of distance functions to training points. We provide generalization bounds for Lipschitz classifiers in terms of the Rademacher complexities of some Lipschitz function classes. The generality of our approach can be seen from the fact that several well-known algorithms are special cases of the Lipschitz classifier, among them the support vector machine, the linear programming machine, and the 1-nearest neighbor classifier.

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


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Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference

Thrun, S., Saul, L., Schölkopf, B.

Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

Abstract
The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

Web [BibTex]

Web [BibTex]


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cDNA-Microarray Technology in Cartilage Research - Functional Genomics of Osteoarthritis [in German]

Aigner, T., Finger, F., Zien, A., Bartnik, E.

Zeitschrift f{\"u}r Orthop{\"a}die und ihre Grenzgebiete, 142(2):241-247, April 2004 (article)

Abstract
Functional genomics represents a new challenging approach in order to analyze complex diseases such as osteoarthritis on a molecular level. The characterization of the molecular changes of the cartilage cells, the chondrocytes, enables a better understanding of the pathomechanisms of the disease. In particular, the identification and characterization of new target molecules for therapeutic intervention is of interest. Also, potential molecular markers for diagnosis and monitoring of osteoarthritis contribute to a more appropriate patient management. The DNA-microarray technology complements (but does not replace) biochemical and biological research in new disease-relevant genes. Large-scale functional genomics will identify molecular networks such as yet identified players in the anabolic-catabolic balance of articular cartilage as well as disease-relevant intracellular signaling cascades so far rather unknown in articular chondrocytes. However, at the moment it is also important to recognize the limitations of the microarray technology in order to avoid over-interpretation of the results. This might lead to misleading results and prevent to a significant extent a proper use of the potential of this technology in the field of osteoarthritis.

[BibTex]

[BibTex]


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A Compression Approach to Support Vector Model Selection

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

Journal of Machine Learning Research, 5, pages: 293-323, April 2004 (article)

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 a code built from the separating hyperplane. The main idea is to relate the coding precision to geometrical concepts such as the width of the margin or the shape of the data in the feature space. The so derived compression coefficients combine well known quantities such as the radius-margin term 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 benchmark data sets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.

PDF [BibTex]

PDF [BibTex]


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Injecting noise for analysing the stability of ICA components

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

Signal Processing, 84(2):255-266, February 2004 (article)

Abstract
Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliability and the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well by our method. In a toy experiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratory data analysis of real-world data.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Independent component analysis and beyond

Oja, E., Harmeling, S., Almeida, L.

Signal Processing, 84(2):215-216, February 2004 (article)

PDF DOI [BibTex]

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, 17(1):127-141, January 2004 (article)

Abstract
In Support Vector (SV) regression, a parameter ν controls the number of Support Vectors and the number of points that come to lie outside of the so-called var epsilon-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of ν that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression. As a side effect of the experiments, valuable information about the generalization behavior of the remaining SVM parameters and their dependencies is gained. The experimental findings are valid even for complex ‘real-world’ data sets. Based on our results on the role of the ν-SVM parameters, we discuss various model selection methods.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Constant infusion H215O PET and acetazolamide challenge in the assessment of the cerebral perfusion status

Weber, B., Westera, G., Treyer, V., Burger, C., Kahn, N., Buck, A.

Journal of Nuclear Medicine, (45):1344-1349, 2004 (article)

[BibTex]

[BibTex]


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Protein ranking: from local to global structure in the protein similarity network

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

Proceedings of the National Academy of Science, 101(17):6559-6563, 2004 (article)

Abstract
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a pre-computed, weighted network. The resulting ranking algorithm, evaluated using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as PSI-BLAST.

Web [BibTex]

Web [BibTex]


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Asymptotic Properties of the Fisher Kernel

Tsuda, K., Akaho, S., Kawanabe, M., Müller, K.

Neural Computation, 16(1):115-137, 2004 (article)

PDF [BibTex]

PDF [BibTex]


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Some observations on the effects of slant and texture type on slant-from-texture

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

Vision Research, 44(13):1511-1535, 2004 (article)

Abstract
We measure the performance of five subjects in a slant-discrimination task for differently textured planes. As textures we used uniform lattices, randomly displaced lattices, circles (polka dots), Voronoi tessellations, plaids, 1/f noise, “coherent” noise and a leopard skin-like texture. Our results show: (1) Improving performance with larger slants for all textures. (2) Thus, following from (1), cases of “non-symmetrical” performance around a particular orientation. (3) For orientations sufficiently slanted, the different textures do not elicit major differences in performance, (4) while for orientations closer to the vertical plane there are marked differences between them. (5) These differences allow a rank-order of textures to be formed according to their “helpfulness”– that is, how easy the discrimination task is when a particular texture is mapped on the plane. Polka dots tend to allow the best slant discrimination performance, noise patterns the worst. Two additional experiments were conducted to test the generality of the obtained rank-order. First, the tilt of the planes was rotated to break the axis of gravity present in the original discrimination experiment. Second, the task was changed to a slant report task via probe adjustment. The results of both control experiments confirmed the texture-based rank-order previously obtained. We comment on the importance of these results for depth perception research in general, and in particular the implications our results have for studies of cue combination (sensor fusion) using texture as one of the cues involved.

PDF [BibTex]

PDF [BibTex]


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Protein homology detection using string alignment kernels

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

Bioinformatics, 20(11):1682-1689, 2004 (article)

Abstract
Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVM) are currently the most effective methods for the problem of superfamily recognition in the SCOP database. The performance of SVMs depend critically on the kernel function used to quantify the similarity between sequences. We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the art methods for remote homology detection.

Web DOI [BibTex]

Web DOI [BibTex]


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Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

Tsuda, K., Uda, S., Kin, T., Asai, K.

Neural Processing Letters, 19, pages: 63-72, 2004 (article)

PDF [BibTex]

PDF [BibTex]


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A Tutorial on Support Vector Regression

Smola, A., Schölkopf, B.

Statistics and Computing, 14(3):199-222, 2004 (article)

Web [BibTex]

Web [BibTex]


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Bayesian analysis of the Scatterometer Wind Retrieval Inverse Problem: Some New Approaches

Cornford, D., Csato, L., Evans, D., Opper, M.

Journal of the Royal Statistical Society B, 66, pages: 1-17, 3, 2004 (article)

Abstract
The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem.A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters.We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer.We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution.We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes.This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets.We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.

PDF [BibTex]

PDF [BibTex]


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Feature Selection for Support Vector Machines Using Genetic Algorithms

Fröhlich, H., Chapelle, O., Schölkopf, B.

International Journal on Artificial Intelligence Tools (Special Issue on Selected Papers from the 15th IEEE International Conference on Tools with Artificial Intelligence 2003), 13(4):791-800, 2004 (article)

Web [BibTex]

Web [BibTex]


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Optical Imaging of the Spatiotemporal Dynamics of Cerebral Blood Flow and Oxidative Metabolism in the Rat Barrel Cortex

Weber, B., Burger, C., Wyss, M., von Schulthess, G., Scheffold, F., Buck, A.

European Journal of Neuroscience, 20(10):2664-2670, 2004 (article)

PDF [BibTex]

PDF [BibTex]


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Phenotypic Characterization of Human Chondrocyte Cell Line C-20/A4: A Comparison between Monolayer and Alginate Suspension Culture

Finger, F., Schorle, C., Söder, S., Zien, A., Goldring, M., Aigner, T.

Cells Tissues Organs, 178(2):65-77, 2004 (article)

Abstract
DNA microarray analysis was used to investigate the molecular phenotype of one of the first human chondrocyte cell lines, C-20/A4, derived from juvenile costal chondrocytes by immortalization with origin-defective simian virus 40 large T antigen. Clontech Human Cancer Arrays 1.2 and quantitative PCR were used to examine gene expression profiles of C-20/A4 cells cultured in the presence of serum in monolayer and alginate beads. In monolayer cultures, genes involved in cell proliferation were strongly upregulated compared to those expressed by human adult articular chondrocytes in primary culture. Of the cell cycle-regulated genes, only two, the CDK regulatory subunit and histone H4, were downregulated after culture in alginate beads, consistent with the ability of these cells to proliferate in suspension culture. In contrast, the expression of several genes that are involved in pericellular matrix formation, including MMP-14, COL6A1, fibronectin, biglycan and decorin, was upregulated when the C-20/A4 cells were transferred to suspension culture in alginate. Also, nexin-1, vimentin, and IGFBP-3, which are known to be expressed by primary chondrocytes, were differentially expressed in our study. Consistent with the proliferative phenotype of this cell line, few genes involved in matrix synthesis and turnover were highly expressed in the presence of serum. These results indicate that immortalized chondrocyte cell lines, rather than substituting for primary chondrocytes, may serve as models for extending findings on chondrocyte function not achievable by the use of primary chondrocytes.

[BibTex]

[BibTex]


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Kernel Methods and their Potential Use in Signal Processing

Perez-Cruz, F., Bousquet, O.

IEEE Signal Processing Magazine, (Special issue on Signal Processing for Mining), 2004 (article) Accepted

PostScript [BibTex]

PostScript [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

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)

PDF [BibTex]

PDF [BibTex]


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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]

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]

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]

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]

[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.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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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]

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.

PostScript [BibTex]

PostScript [BibTex]


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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.

PDF [BibTex]

PDF [BibTex]


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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.

PDF PostScript [BibTex]


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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.

GZIP [BibTex]

GZIP [BibTex]


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Marginalized kernels for biological sequences

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

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

PDF [BibTex]

PDF [BibTex]


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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.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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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)

PDF [BibTex]

PDF [BibTex]


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Modeling splicing sites with pairwise correlations

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

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

PDF [BibTex]

PDF [BibTex]


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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.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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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.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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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]

[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.

PDF [BibTex]

PDF [BibTex]


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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.

PDF PostScript [BibTex]

PDF PostScript [BibTex]