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2000


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Entropy Numbers of Linear Function Classes.

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

In 13th Annual Conference on Computational Learning Theory, pages: 309-319, (Editors: N Cesa-Bianchi and S Goldman), Morgan Kaufman, San Fransisco, CA, USA, 13th Annual Conference on Computational Learning Theory (COLT), 2000 (inproceedings)

[BibTex]

2000

[BibTex]


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Kernel method for percentile feature extraction

Schölkopf, B., Platt, J., Smola, A.

(MSR-TR-2000-22), Microsoft Research, 2000 (techreport)

Abstract
A method is proposed which computes a direction in a dataset such that a speci􏰘ed fraction of a particular class of all examples is separated from the overall mean by a maximal margin􏰤 The pro jector onto that direction can be used for class􏰣speci􏰘c feature extraction􏰤 The algorithm is carried out in a feature space associated with a support vector kernel function􏰢 hence it can be used to construct a large class of nonlinear fea􏰣 ture extractors􏰤 In the particular case where there exists only one class􏰢 the method can be thought of as a robust form of principal component analysis􏰢 where instead of variance we maximize percentile thresholds􏰤 Fi􏰣 nally􏰢 we generalize it to also include the possibility of specifying negative examples􏰤

PDF [BibTex]

PDF [BibTex]

1999


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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A., Lengauer, T., Müller, K.

In German Conference on Bioinformatics (GCB 1999), October 1999 (inproceedings)

Abstract
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro­ teins start, the so­called translation initiation sites (TIS). This can be modeled as a classification prob­ lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc­ cessfully incorporate biological prior knowledge by engineering an appropriate kernel function.

Web [BibTex]

1999

Web [BibTex]


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Shrinking the tube: a new support vector regression algorithm

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

In Advances in Neural Information Processing Systems 11, pages: 330-336 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Semiparametric support vector and linear programming machines

Smola, A., Friess, T., Schölkopf, B.

In Advances in Neural Information Processing Systems 11, pages: 585-591 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, Twelfth Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms - Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel PCA and De-noising in feature spaces

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

In Advances in Neural Information Processing Systems 11, pages: 536-542 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as the results provided by kernel PCA live in some high dimensional feature space and need not have pre-images in input space. This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre-images in data reconstruction and de-noising on toy examples as well as on real world data.

PDF Web [BibTex]

PDF Web [BibTex]


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Estimating the support of a high-dimensional distribution

Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.

(MSR-TR-99-87), Microsoft Research, 1999 (techreport)

Web [BibTex]

Web [BibTex]


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Classifying LEP data with support vector algorithms.

Vannerem, P., Müller, K., Smola, A., Schölkopf, B., Söldner-Rembold, S.

In Artificial Intelligence in High Energy Nuclear Physics 99, Artificial Intelligence in High Energy Nuclear Physics 99, 1999 (inproceedings)

[BibTex]

[BibTex]


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Generalization Bounds via Eigenvalues of the Gram matrix

Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R.

(99-035), NeuroCOLT, 1999 (techreport)

[BibTex]

[BibTex]


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Classification on proximity data with LP-machines

Graepel, T., Herbrich, R., Schölkopf, B., Smola, A., Bartlett, P., Müller, K., Obermayer, K., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 304-309, Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Kernel-dependent support vector error bounds

Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 103-108 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Linear programs for automatic accuracy control in regression

Smola, A., Schölkopf, B., Rätsch, G.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 575-580 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Regularized principal manifolds.

Smola, A., Williamson, R., Mika, S., Schölkopf, B.

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 214-229 , Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Entropy numbers, operators and support vector kernels.

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

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 285-299, Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Sparse kernel feature analysis

Smola, A., Mangasarian, O., Schölkopf, B.

(99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

PostScript [BibTex]

PostScript [BibTex]


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Is the Hippocampus a Kalman Filter?

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

In Proceedings of the Pacific Symposium on Biocomputing, 3, pages: 619-630, Proceedings of the Pacific Symposium on Biocomputing, 1999 (inproceedings)

[BibTex]

[BibTex]


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A Comparison of Artificial Neural Networks and Cluster Analysis for Typing Biometrics Authentication

Maisuria, K., Ong, CS., Lai, .

In unknown, pages: 9999-9999, International Joint Conference on Neural Networks, 1999 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Fisher discriminant analysis with kernels

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

In Proceedings of the 1999 IEEE Signal Processing Society Workshop, 9, pages: 41-48, (Editors: Y-H Hu and J Larsen and E Wilson and S Douglas), IEEE, Neural Networks for Signal Processing IX, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]

1998


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Navigation mit Schnappschüssen

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

In Mustererkennung 1998, pages: 421-428, (Editors: P Levi and R-J Ahlers and F May and M Schanz), Springer, Berlin, Germany, 20th DAGM-Symposium, October 1998 (inproceedings)

Abstract
Es wird ein biologisch inspirierter Algorithmus vorgestellt, mit dem sich ein Ort wiederfinden l{\"a}sst, an dem vorher eine 360-Grad-Ansicht der Umgebung aufgenommen wurde. Die Zielrichtung wird aus der Verschiebung der Bildposition der umgebenden Landmarken im Vergleich zum Schnappschuss berechnet. Die Konvergenzeigenschaften des Algorithmus werden mathematisch untersucht und auf mobilen Robotern getestet.

PDF Web [BibTex]

1998

PDF Web [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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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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Generalization bounds and learning rates for Regularized principal manifolds

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

NeuroCOLT, 1998, NeuroColt2-TR 1998-027 (techreport)

[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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Generalization Bounds for Convex Combinations of Kernel Functions

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

Royal Holloway College, 1998 (techreport)

[BibTex]

[BibTex]


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Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators

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

(19), NeuroCOLT, 1998, Accepted for publication in IEEE Transactions on Information Theory (techreport)

[BibTex]

[BibTex]


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Quantization Functionals and Regularized PrincipalManifolds

Smola, A., Mika, S., Schölkopf, B.

NeuroCOLT, 1998, NC2-TR-1998-028 (techreport)

[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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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]


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Support Vector Machine Reference Manual

Saunders, C., Stitson, M., Weston, J., Bottou, L., Schölkopf, B., Smola, A.

(CSD-TR-98-03), Department of Computer Science, Royal Holloway, University of London, 1998 (techreport)

PostScript [BibTex]

PostScript [BibTex]

1997


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The view-graph approach to visual navigation and spatial memory

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

In Artificial Neural Networks: ICANN ’97, pages: 751-756, (Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks, October 1997 (inproceedings)

Abstract
This paper describes a purely visual navigation scheme based on two elementary mechanisms (piloting and guidance) and a graph structure combining individual navigation steps controlled by these mechanisms. In robot experiments in real environments, both mechanisms have been tested, piloting in an open environment and guidance in a maze with restricted movement opportunities. The results indicate that navigation and path planning can be brought about with these simple mechanisms. We argue that the graph of local views (snapshots) is a general and biologically plausible means of representing space and integrating the various mechanisms of map behaviour.

PDF PDF DOI [BibTex]

1997

PDF PDF DOI [BibTex]


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Predicting time series with support vector machines

Müller, K., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.

In Artificial Neural Networks: ICANN’97, pages: 999-1004, (Editors: Schölkopf, B. , C.J.C. Burges, A.J. Smola), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks , October 1997 (inproceedings)

Abstract
Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 29%.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Predicting time series with support vectur machines

Müller, K., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.

In Artificial neural networks: ICANN ’97, pages: 999-1004, (Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks , October 1997 (inproceedings)

Abstract
Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 29%.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Kernel principal component analysis

Schölkopf, B., Smola, A., Müller, K.

In Artificial neural networks: ICANN ’97, LNCS, vol. 1327, pages: 583-588, (Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks, October 1997 (inproceedings)

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 highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Homing by parameterized scene matching

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

In Proceedings of the 4th European Conference on Artificial Life, (Eds.) P. Husbands, I. Harvey. MIT Press, Cambridge 1997, pages: 236-245, (Editors: P Husbands and I Harvey), MIT Press, Cambridge, MA, USA, 4th European Conference on Artificial Life (ECAL97), July 1997 (inproceedings)

Abstract
In visual homing tasks, animals as well as robots can compute their movements from the current view and a snapshot taken at a home position. Solving this problem exactly would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. We propose a homing scheme that dispenses with accurate distance information by using parameterized disparity fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that the approximation does not prevent the scheme from approaching the goal with arbitrary accuracy. Mobile robot experiments are used to demonstrate the practical feasibility of the approach.

PDF [BibTex]

PDF [BibTex]


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Improving the accuracy and speed of support vector learning machines

Burges, C., Schölkopf, B.

In Advances in Neural Information Processing Systems 9, pages: 375-381, (Editors: M Mozer and MJ Jordan and T Petsche), MIT Press, Cambridge, MA, USA, Tenth Annual Conference on Neural Information Processing Systems (NIPS), May 1997 (inproceedings)

Abstract
Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for illposed problems . Against this very general backdrop any methods for improving the generalization performance, or for improving the speed in test phase of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem The method for improving generalization performance the "virtual support vector" method does so by incorporating known invariances of the problem This method achieves a drop in the error rate on 10.000 NIST test digit images of 1,4 % to 1 %. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance achieving 1,1 % error . The virtual support vector method is applicable to any SVM problem with known invariances The reduced set method is applicable to any support vector machine .

PDF Web [BibTex]

PDF Web [BibTex]


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Homing by parameterized scene matching

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

(46), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, Febuary 1997 (techreport)

Abstract
In visual homing tasks, animals as well as robots can compute their movements from the current view and a snapshot taken at a home position. Solving this problem exactly would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. We propose a homing scheme that dispenses with accurate distance information by using parameterized disparity fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that the approximation does not prevent the scheme from approaching the goal with arbitrary accuracy. Mobile robot experiments are used to demonstrate the practical feasibility of the approach.

[BibTex]

[BibTex]


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Learning view graphs for robot navigation

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

In Proceedings of the 1st Intl. Conf. on Autonomous Agents, pages: 138-147, (Editors: Johnson, W.L.), ACM Press, New York, NY, USA, First International Conference on Autonomous Agents (AGENTS '97), Febuary 1997 (inproceedings)

Abstract
We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]

1996


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The DELVE user manual

Rasmussen, CE., Neal, RM., Hinton, GE., van Camp, D., Revow, M., Ghahramani, Z., Kustra, R., Tibshirani, R.

Department of Computer Science, University of Toronto, December 1996 (techreport)

Abstract
This manual describes the preliminary release of the DELVE environment. Some features described here have not yet implemented, as noted. Support for regression tasks is presently somewhat more developed than that for classification tasks. We recommend that you exercise caution when using this version of DELVE for real work, as it is possible that bugs remain in the software. We hope that you will send us reports of any problems you encounter, as well as any other comments you may have on the software or manual, at the e-mail address below. Please mention the version number of the manual and/or the software with any comments you send.

GZIP [BibTex]

1996

GZIP [BibTex]


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Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Schölkopf, B., Smola, A., Müller, K.

(44), Max Planck Institute for Biological Cybernetics Tübingen, December 1996, This technical report has also been published elsewhere (techreport)

Abstract
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we 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 5-pixel products in 16 x 16 images. We give the derivation of the method, along with a discussion of other techniques which can be made nonlinear with the kernel approach; and present first experimental results on nonlinear feature extraction for pattern recognition.

[BibTex]

[BibTex]


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Quality Prediction of Steel Products using Neural Networks

Shin, H., Jhee, W.

In Proc. of the Korean Expert System Conference, pages: 112-124, Korean Expert System Society Conference, November 1996 (inproceedings)

[BibTex]

[BibTex]


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Comparison of view-based object recognition algorithms using realistic 3D models

Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., Vetter, T.

In Artificial Neural Networks: ICANN 96, LNCS, vol. 1112, pages: 251-256, Lecture Notes in Computer Science, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996 (inproceedings)

Abstract
Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of the objects. Classification performance is assessed using a high number of images generated by a computer graphics system under precisely controlled conditions. Training- and test-images show a set of 25 realistic three-dimensional models of chairs from viewing directions spread over the upper half of the viewing sphere. The percentage of correct identification of all 25 objects is measured.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Learning View Graphs for Robot Navigation

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

(33), Max Planck Institute for Biological Cybernetics, Tübingen,, July 1996 (techreport)

Abstract
We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach.

[BibTex]

[BibTex]


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Incorporating invariances in support vector learning machines

Schölkopf, B., Burges, C., Vapnik, V.

In Artificial Neural Networks: ICANN 96, LNCS vol. 1112, pages: 47-52, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996, volume 1112 of Lecture Notes in Computer Science (inproceedings)

Abstract
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.

PDF DOI [BibTex]

PDF DOI [BibTex]


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A practical Monte Carlo implementation of Bayesian learning

Rasmussen, CE.

In Advances in Neural Information Processing Systems 8, pages: 598-604, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 datalimited tasks from real world domains.

PDF Web [BibTex]

PDF Web [BibTex]


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Gaussian Processes for Regression

Williams, CKI., Rasmussen, CE.

In Advances in neural information processing systems 8, pages: 514-520, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

PDF Web [BibTex]

PDF Web [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]