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2001


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The control structure of artificial creatures

Zhou, D., Dai, R.

Artificial Life and Robotics, 5(3), 2001, invited article (article)

Web [BibTex]

2001

Web [BibTex]


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Markovian domain fingerprinting: statistical segmentation of protein sequences

Bejerano, G., Seldin, Y., Margalit, H., Tishby, N.

Bioinformatics, 17(10):927-934, 2001 (article)

PDF Web [BibTex]

PDF Web [BibTex]

1999


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Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten

Schölkopf, B., Müller, K., Smola, A.

Informatik - Forschung und Entwicklung, 14(3):154-163, September 1999 (article)

Abstract
We describe recent developments and results of statistical learning theory. In the framework of learning from examples, two factors control generalization ability: explaining the training data by a learning machine of a suitable complexity. We describe kernel algorithms in feature spaces as elegant and efficient methods of realizing such machines. Examples thereof are Support Vector Machines (SVM) and Kernel PCA (Principal Component Analysis). More important than any individual example of a kernel algorithm, however, is the insight that any algorithm that can be cast in terms of dot products can be generalized to a nonlinear setting using kernels. Finally, we illustrate the significance of kernel algorithms by briefly describing industrial and academic applications, including ones where we obtained benchmark record results.

PDF PDF DOI [BibTex]

1999

PDF PDF DOI [BibTex]


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Input space versus feature space in kernel-based methods

Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K., Rätsch, G., Smola, A.

IEEE Transactions On Neural Networks, 10(5):1000-1017, September 1999 (article)

Abstract
This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using the example of the class of inhomogeneous polynomial kernels, which are often used in SV pattern recognition. We then discuss the connection between feature space and input space by dealing with the question of how one can, given some vector in feature space, find a preimage (exact or approximate) in input space. We describe algorithms to tackle this issue, and show their utility in two applications of kernel methods. First, we use it to reduce the computational complexity of SV decision functions; second, we combine it with the kernel PCA algorithm, thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real-world data.

Web DOI [BibTex]

Web DOI [BibTex]


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p73 and p63 are homotetramers capable of weak heterotypic interactions with each other but not with p53.

Davison, T., Vagner, C., Kaghad, M., Ayed, A., Caput, D., CH, ..

Journal of Biological Chemistry, 274(26):18709-18714, June 1999 (article)

Abstract
Mutations in the p53 tumor suppressor gene are the most frequent genetic alterations found in human cancers. Recent identification of two human homologues of p53 has raised the prospect of functional interactions between family members via a conserved oligomerization domain. Here we report in vitro and in vivo analysis of homo- and hetero-oligomerization of p53 and its homologues, p63 and p73. The oligomerization domains of p63 and p73 can independently fold into stable homotetramers, as previously observed for p53. However, the oligomerization domain of p53 does not associate with that of either p73 or p63, even when p53 is in 15-fold excess. On the other hand, the oligomerization domains of p63 and p73 are able to weakly associate with one another in vitro. In vivo co-transfection assays of the ability of p53 and its homologues to activate reporter genes showed that a DNA-binding mutant of p53 was not able to act in a dominant negative manner over wild-type p73 or p63 but that a p73 mutant could inhibit the activity of wild-type p63. These data suggest that mutant p53 in cancer cells will not interact with endogenous or exogenous p63 or p73 via their respective oligomerization domains. It also establishes that the multiple isoforms of p63 as well as those of p73 are capable of interacting via their common oligomerization domain.

Web [BibTex]

Web [BibTex]


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Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

Balakrishnan, K., Bousquet, O., Honavar, V.

Adaptive Behavior, 7(2):173-216, 1999 (article)

[BibTex]


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SVMs for Histogram Based Image Classification

Chapelle, O., Haffner, P., Vapnik, V.

IEEE Transactions on Neural Networks, (9), 1999 (article)

Abstract
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form $K(mathbf{x},mathbf{y})=e^{-rhosum_i |x_i^a-y_i^a|^{b}}$ with $aleq 1$ and $b leq 2$ are evaluated on the classification of images extracted from the Corel Stock Photo Collection and shown to far outperform traditional polynomial or Gaussian RBF kernels. Moreover, we observed that a simple remapping of the input $x_i rightarrow x_i^a$ improves the performance of linear SVMs to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.

GZIP [BibTex]

GZIP [BibTex]

1998


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Book Review: An Introduction to Fuzzy Logic for Practical Applications

Peters, J.

K{\"u}nstliche Intelligenz (KI), 98(4):60-60, November 1998 (article)

[BibTex]

1998

[BibTex]


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Where did I take that snapshot? Scene-based homing by image matching

Franz, M., Schölkopf, B., Bülthoff, H.

Biological Cybernetics, 79(3):191-202, October 1998 (article)

Abstract
In homing tasks, the goal is often not marked by visible objects but must be inferred from the spatial relation to the visual cues in the surrounding scene. The exact computation of the goal direction would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. However, if prior assumptions about typical distance distributions are used, a snapshot taken at the goal suffices to compute the goal direction from the current view. We show that most existing approaches to scene-based homing implicitly assume an isotropic landmark distribution. As an alternative, we propose a homing scheme that uses parameterized displacement fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that both approximations do not prevent the schemes from approaching the goal with arbitrary accuracy, but lead to different errors in the computed goal direction. Mobile robot experiments are used to test the theoretical predictions and to demonstrate the practical feasibility of the new approach.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

Smola, A., Schölkopf, B.

Algorithmica, 22(1-2):211-231, September 1998 (article)

Abstract
We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above are formulated as constrained optimization problems. Previous approaches such as ridge regression, support vector methods, and regularization networks are included as special cases. We show connections between the cost function and some properties up to now believed to apply to support vector machines only. For appropriately chosen cost functions, the optimal solution of all the problems described above can be found by solving a simple quadratic programming problem.

PDF DOI [BibTex]


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The moon tilt illusion

Schölkopf, B.

Perception, 27(10):1229-1232, August 1998 (article)

Abstract
Besides the familiar moon illusion [eg Hershenson, 1989 The Moon illusion (Hillsdale, NJ: Lawrence Erlbaum Associates)], wherein the moon appears bigger when it is close to the horizon, there is a less known illusion which causes the moon‘s illuminated side to appear turned away from the direction of the sun. An experiment documenting the effect is described, and a possible explanation is put forward.

Web DOI [BibTex]

Web DOI [BibTex]


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Characterization of the oligomerization defects of two p53 mutants found in families with Li-Fraumeni and Li-Fraumeni-like syndrome.

Davison, T., Yin, P., Nie, E., Kay, C., CH, ..

Oncogene, 17(5):651-656, August 1998 (article)

Abstract
Recently two germline mutations in the oligomerization domain of p53 have been identified in patients with Li-Fraumeni and Li-Fraumeni-like Syndromes. We have used biophysical and biochemical methods to characterize these two mutants in order to better understand their functional defects and the role of the p53 oligomerization domain (residues 325-355) in oncogenesis. We find that residues 310-360 of the L344P mutant are monomeric, apparently unfolded and cannot interact with wild-type (WT) p53. The full length L344P protein is unable to bind sequence specifically to DNA and is therefore an inactive, but not a dominant negative mutant. R337C, on the other hand, can form dimers and tetramers, can hetero-oligomerize with WTp53 and can bind to a p53 consensus element. However, the thermal stability of R337C is much lower than that of WTp53 and at physiological temperatures more than half of this mutant is less than tetrameric. Thus, the R337C mutant retains some functional activity yet leads to a predisposition to cancer, suggesting that even partial inactivation of p53 oligomerization is sufficient for accelerated tumour progression.

Web [BibTex]


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Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Schölkopf, B., Smola, A., Müller, K.

Neural Computation, 10(5):1299-1319, July 1998 (article)

Abstract
A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

Web DOI [BibTex]

Web DOI [BibTex]


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SVMs — a practical consequence of learning theory

Schölkopf, B.

IEEE Intelligent Systems and their Applications, 13(4):18-21, July 1998 (article)

Abstract
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Support vector machines

Hearst, M., Dumais, S., Osman, E., Platt, J., Schölkopf, B.

IEEE Intelligent Systems and their Applications, 13(4):18-28, July 1998 (article)

Abstract
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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The connection between regularization operators and support vector kernels.

Smola, A., Schölkopf, B., Müller, K.

Neural Networks, 11(4):637-649, June 1998 (article)

Abstract
n this paper a correspondence is derived between regularization operators used in regularization networks and support vector kernels. We prove that the Green‘s Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties. Moreover, the paper provides an analysis of currently used support vector kernels in the view of regularization theory and corresponding operators associated with the classes of both polynomial kernels and translation invariant kernels. The latter are also analyzed on periodical domains. As a by-product we show that a large number of radial basis functions, namely conditionally positive definite functions, may be used as support vector kernels.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning view graphs for robot navigation

Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H.

Autonomous Robots, 5(1):111-125, March 1998 (article)

Abstract
We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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No role for motion blur in either motion detection or motion based image segmentation

Wichmann, F., Henning, G.

Journal of the Optical Society of America A, 15 (2), pages: 297-306, 1998 (article)

Abstract
Determined the influence of high-spatial-frequency losses induced by motion on motion detection and on motion-based image segmentation. Motion detection and motion-based segmentation tasks were performed with either spectrally low-pass or spectrally broadband stimuli. Performance on these tasks was compared with a condition having no motion but in which form differences mimicked the perceptual loss of high spatial frequencies produced by motion. This allowed the relative salience of motion and motion-induced blur to be determined. Neither image segmentation nor motion detection was sensitive to the high-spatial-frequency content of the stimuli. Thus the change in perceptual form produced in moving stimuli is not normally used as a cue either for motion detection or for motion-based image segmentation in ordinary situations.

PDF [BibTex]

PDF [BibTex]


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PET with 18fluorodeoxyglucose and hexamethylpropylene amine oxime SPECT in late whiplash syndrome

Bicik, I., Radanov, B., Schaefer, N., Dvorak, J., Blum, B., Weber, B., Burger, C., von Schulthess, G., Buck, A.

Neurology, 51, pages: 345-350, 1998 (article)

[BibTex]

[BibTex]


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Changes of cerebral blood flow during short-term exposure to normobaric hypoxia

Buck, A., Schirlo, C., Jasinsky, V., Weber, B., Burger, C., von Schulthess, G., Koller, E., Pavlicek, V.

J Cereb Blood Flow Metab, 18, pages: 906-910, 1998 (article)

[BibTex]

[BibTex]


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Support Vector methods in learning and feature extraction

Schölkopf, B., Smola, A., Müller, K., Burges, C., Vapnik, V.

Ninth Australian Conference on Neural Networks, pages: 72-78, (Editors: T. Downs, M. Frean and M. Gallagher), 1998 (talk)

[BibTex]

[BibTex]


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Funktionelle Magnetresonanztomographie in der psychopathologischen Forschung.

Spitzer, M., Kammer, T., Bellemann, M., Brix, G., Layer, B., Maier, S., Kischka, U., Gückel, F.

Fortschritte der Neurologie Psychiatrie, 66, pages: 241-258, 1998 (article)

Abstract
Mental disorders are characterised by psychopathological symptoms which correspond to functional brain states. Functional magnetic resonance imaging (fMRI) is used for the non-invasive study of cerebral activation patterns in man. First of all, the neurobiological principles and presuppositions of the method are outlined. Results from the Heidelberg imaging lab on several simple sensorimotor tasks as well as higher cognitive functions, such as working and semantic memory, are then presented. Thereafter, results from preliminary fMRI studies of psychopathological symptoms are discussed, with emphasis on hallucinations, psychomotoric phenomena, emotions, as well as obsessions and compulsions. Functional MRI is limited by the physics underlying the method, as well as by practical constraints regarding its use in conjunction with mentally ill patients. Within this framework, the problems of signal-to-noise ratio, data analysis strategies, motion correction, and neurovascular coupling are considered. Because of the rapid development of the field of fMRI, maps of higher cognitive functions and their respective pathology seem to be coming within easy reach.

[BibTex]

[BibTex]