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1998


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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]

1998

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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Prior knowledge in support vector kernels

Schölkopf, B., Simard, P., Smola, A., Vapnik, V.

In Advances in Neural Information Processing Systems 10, pages: 640-646 , (Editors: M Jordan and M Kearns and S Solla ), MIT Press, Cambridge, MA, USA, Eleventh Annual Conference on Neural Information Processing (NIPS), June 1998 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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From regularization operators to support vector kernels

Smola, A., Schölkopf, B.

In Advances in Neural Information Processing Systems 10, pages: 343-349, (Editors: M Jordan and M Kearns and S Solla), MIT Press, Cambridge, MA, USA, 11th Annual Conference on Neural Information Processing (NIPS), June 1998 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Qualitative Modeling for Data Miner’s Requirements

Shin, H., Jhee, W.

In Proc. of the Korean Management Information Systems, pages: 65-73, Conference on the Korean Management Information Systems, April 1998 (inproceedings)

[BibTex]

[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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Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces.

Schölkopf, B., Knirsch, P., Smola, A., Burges, C.

In Mustererkennung 1998, pages: 125-132, Informatik aktuell, (Editors: P Levi and M Schanz and R-J Ahlers and F May), Springer, Berlin, Germany, 20th DAGM-Symposium, 1998 (inproceedings)

Abstract
Kernel-based learning methods provide their solutions as expansions in terms of a kernel. We consider the problem of reducing the computational complexity of evaluating these expansions by approximating them using fewer terms. As a by-product, we point out a connection between clustering and approximation in reproducing kernel Hilbert spaces generated by a particular class of kernels.

Web [BibTex]

Web [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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Kernel PCA pattern reconstruction via approximate pre-images.

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

In 8th International Conference on Artificial Neural Networks, pages: 147-152, Perspectives in Neural Computing, (Editors: L Niklasson and M Boden and T Ziemke), Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 1998 (inproceedings)

[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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Convex Cost Functions for Support Vector Regression

Smola, A., Schölkopf, B., Müller, K.

In 8th International Conference on Artificial Neural Networks, pages: 99-104, Perspectives in Neural Computing, (Editors: L Niklasson and M Boden and T Ziemke), Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 1998 (inproceedings)

[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]


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Support vector regression with automatic accuracy control.

Schölkopf, B., Bartlett, P., Smola, A., Williamson, R.

In ICANN'98, pages: 111-116, Perspectives in Neural Computing, (Editors: L Niklasson and M Boden and T Ziemke), Springer, Berlin, Germany, International Conference on Artificial Neural Networks (ICANN'98), 1998 (inproceedings)

[BibTex]

[BibTex]


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General cost functions for support vector regression.

Smola, A., Schölkopf, B., Müller, K.

In Ninth Australian Conference on Neural Networks, pages: 79-83, (Editors: T Downs and M Frean and M Gallagher), 9th Australian Conference on Neural Networks (ACNN'98), 1998 (inproceedings)

[BibTex]

[BibTex]


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Asymptotically optimal choice of varepsilon-loss for support vector machines.

Smola, A., Murata, N., Schölkopf, B., Müller, K.

In 8th International Conference on Artificial Neural Networks, pages: 105-110, Perspectives in Neural Computing, (Editors: L Niklasson and M Boden and T Ziemke), Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 1998 (inproceedings)

[BibTex]

[BibTex]

1995


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View-based cognitive map learning by an autonomous robot

Mallot, H., Bülthoff, H., Georg, P., Schölkopf, B., Yasuhara, K.

In Proceedings International Conference on Artificial Neural Networks, vol. 2, pages: 381-386, (Editors: Fogelman-Soulié, F.), EC2, Paris, France, Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN '95), October 1995 (inproceedings)

Abstract
This paper presents a view-based approach to map learning and navigation in mazes. By means of graph theory we have shown that the view-graph is a sufficient representation for map behaviour such as path planning. A neural network for unsupervised learning of the view-graph from sequences of views is constructed. We use a modified Kohonen (1988) learning rule that transforms temporal sequence (rather than featural similarity) into connectedness. In the main part of the paper, we present a robot implementation of the scheme. The results show that the proposed network is able to support map behaviour in simple environments.

PDF [BibTex]

1995

PDF [BibTex]


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Extracting support data for a given task

Schölkopf, B., Burges, C., Vapnik, V.

In First International Conference on Knowledge Discovery & Data Mining (KDD-95), pages: 252-257, (Editors: UM Fayyad and R Uthurusamy), AAAI Press, Menlo Park, CA, USA, August 1995 (inproceedings)

Abstract
We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small (k: 4%) subsets of the data base. This finding opens up the possibiiity of compressing data bases significantly by disposing of the data which is not important for the solution of a given task. In addition, we show that the theory allows us to predict the classifier that will have the best generalization ability, based solely on performance on the training set and characteristics of the learning machines. This finding is important for cases where the amount of available data is limited.

PDF [BibTex]

PDF [BibTex]


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View-Based Cognitive Mapping and Path Planning

Schölkopf, B., Mallot, H.

Adaptive Behavior, 3(3):311-348, January 1995 (article)

Abstract
This article presents a scheme for learning a cognitive map of a maze from a sequence of views and movement decisions. The scheme is based on an intermediate representation called the view graph, whose nodes correspond to the views whereas the labeled edges represent the movements leading from one view to another. By means of a graph theoretical reconstruction method, the view graph is shown to carry complete information on the topological and directional structure of the maze. Path planning can be carried out directly in the view graph without actually performing this reconstruction. A neural network is presented that learns the view graph during a random exploration of the maze. It is based on an unsupervised competitive learning rule translating temporal sequence (rather than similarity) of views into connectedness in the network. The network uses its knowledge of the topological and directional structure of the maze to generate expectations about which views are likely to be encountered next, improving the view-recognition performance. Numerical simulations illustrate the network's ability for path planning and the recognition of views degraded by random noise. The results are compared to findings of behavioral neuroscience.

Web DOI [BibTex]

Web DOI [BibTex]


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Suppression and creation of chaos in a periodically forced Lorenz system.

Franz, MO., Zhang, MH.

Physical Review, E 52, pages: 3558-3565, 1995 (article)

Abstract
Periodic forcing is introduced into the Lorenz model to study the effects of time-dependent forcing on the behavior of the system. Such a nonautonomous system stays dissipative and has a bounded attracting set which all trajectories finally enter. The possible kinds of attracting sets are restricted to periodic orbits and strange attractors. A large-scale survey of parameter space shows that periodic forcing has mainly three effects in the Lorenz system depending on the forcing frequency: (i) Fixed points are replaced by oscillations around them; (ii) resonant periodic orbits are created both in the stable and the chaotic region; (iii) chaos is created in the stable region near the resonance frequency and in periodic windows. A comparison to other studies shows that part of this behavior has been observed in simulations of higher truncations and real world experiments. Since very small modulations can already have a considerable effect, this suggests that periodic processes such as annual or diurnal cycles should not be omitted even in simple climate models.

[BibTex]

[BibTex]