PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Methods for Implicit Surface Modeling
Bernhard Schölkopf, J. Giesen and S. Spalinger
In: Advances in Neural Information Processing Systems 17, 8-13 Dec 2003, Vancouver / Whistler, Canada.

Abstract

We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space and then determining regions in terms of hyperplanes.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:[Note: An earlier version of this work appeared as MPI-TR No. 125, 2004]
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1019
Deposited By:Bernhard Schölkopf
Deposited On:16 July 2005