Kernel Methods for Implicit Surface Modeling

Schölkopf, B; Giesen, J; Spalinger, S

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Kernel Methods for Implicit Surface Modeling

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://papers.nips.cc/paper/2724-kernel-methods-for-implicit-surface-modeling.pdf
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引用

Schölkopf, B., Giesen, J., & Spalinger, S. (2005). Kernel Methods for Implicit Surface Modeling. In L., Saul, Y., Weiss, & L., Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 1193-1200). Cambridge, MA, USA: MIT Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-D521-0

要旨

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.