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1999


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Entropy numbers, operators and support vector kernels.

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

In Advances in Kernel Methods - Support Vector Learning, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

1999

[BibTex]

1998


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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]

1998

[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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Support-Vektor-Lernen

Schölkopf, B.

In Ausgezeichnete Informatikdissertationen 1997, pages: 135-150, (Editors: G Hotz and H Fiedler and P Gorny and W Grass and S Hölldobler and IO Kerner and R Reischuk), Teubner Verlag, Stuttgart, 1998 (inbook)

[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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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]

1997

[BibTex]

1994


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View-based cognitive mapping and path planning

Schölkopf, B., Mallot, H.

(7), Max Planck Institute for Biological Cybernetics Tübingen, November 1994, This technical report has also been published elsewhere (techreport)

Abstract
We present 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. We show that this representation carries sufficient information to reconstruct the topological and directional structure of the maze. Moreover, we present a neural network that learns the view graph during a random exploration of the maze. We use a unsupervised competitive learning rule which translates 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 perceived next, improving the view recognition performance. We provide an additional mechanism which uses the map to find paths between arbitrary points of the previously explored environment. The results are compared to findings of behavioural neuroscience.

[BibTex]

1994

[BibTex]