nips nips2004 nips2004-92 nips2004-92-reference knowledge-graph by maker-knowledge-mining
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Author: Joachim Giesen, Simon Spalinger, Bernhard Schölkopf
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. 1
[1] J. Carr, R. Beatson, J. Cherrie, T. Mitchell, W. Fright, B. McCallum, and T. Evans. Reconstruction and representation of 3D objects with radial basis functions. In Proc. 28th Ann. Conf. Computer Graphics and Interactive Techniques, pages 67–76. 2001.
[2] C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.
[3] O. Chapelle and B. Sch¨ lkopf. Incorporating invariances in nonlinear SVMs. In T.G. o Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.
[4] J. Giesen and M. John. Surface reconstruction based on a dynamical system. Computer Graphics Forum, 21(3):363–371, 2002.
[5] T. Lewiner, H. Lopes, A. Wilson, and G. Tavares. Efficient implementation of marching cubes cases with topological guarantee. Journal of Graphics Tools, 8:1–15, 2003.
[6] S. Osher and N. Paragios. Geometric Level Set Methods. Springer, New York, 2003.
[7] M. Pauly, R. Keiser, and M. Gross. Multi-scale feature extraction on point-sampled surfaces. Computer Graphics Forum, 22(3):281–289, 2003.
[8] B. Sch¨ lkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating o the support of a high-dimensional distribution. Neural Computation, 13:1443–1471, 2001.
[9] I. Steinwart. Sparseness of support vector machines—some asymptotically sharp bounds. In S. Thrun, L. Saul, and B. Sch¨ lkopf, editors, Advances in Neural Ino formation Processing Systems 16. MIT Press, Cambridge, MA, 2004.
[10] D. M. J. Tax and R. P. W. Duin. Support vector data description. Machine Learning, 54:45–66, 2004.
[11] V. N. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.