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Kernel Methods for Implicit Surface Modeling
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.
@inproceedings{2814, title = {Kernel Methods for Implicit Surface Modeling}, journal = {Advances in Neural Information Processing Systems}, booktitle = {Advances in Neural Information Processing Systems 17}, 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.}, pages = {1193-1200}, editors = {LK Saul and Y Weiss and L Bottou}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = jul, year = {2005}, slug = {2814}, author = {Sch{\"o}lkopf, B. and Giesen, J. and Spalinger, S.}, month_numeric = {7} }