Detecting and approximating fault lines from randomly scattered data

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[1] G. Allasia, R. Besenghi and A. De Rossi, A scattered data approximation scheme for the detection
of fault lines, in: Mathematical Methods for Curves and Surfaces: Oslo 2000, eds. T. Lyche and
L.L. Schumaker (Vanderbilt Univ. Press, Nashville, TN, 2000) pp. 25–34.
[2] D. Apprato and C. Gout, A result about scale transformation families in approximation: Application
to surface fitting from rapidly varying data, Numer. Algorithms 23 (2000) 263–279.
[3] E. Arge and M. Floater, Approximating scattered data with discontinuities, Numer. Algorithms 8
(1994) 149–166.
[4] I. Barrodale and F.D.K. Roberts, Solution of an overdetermined system of linear equations in the 1
norm (Algorithm 478), Comm. of the ACM 17(6) (1974) 319–320.
[5] R. Besenghi and G. Allasia, Scattered data near-interpolation with applications to discontinuous surfaces,
in: Curve and Surface Fitting, eds. A. Cohen, C. Rabut and L.L. Schumaker (Vanderbilt Univ.
Press, Nashville, TN, 2000) pp. 75–84.
[6] J. Duchon, Splines minimising rotation-invariant seminorms in Sobolev spaces, in: Lecture Notes in
Mathematics, Vol. 571, eds. W. Schempp and K. Zeller (Springer, Berlin, 1977) pp. 85–100.
[7] N. Dyn, K. Hormann, S.-J. Kim and D. Levin, Optimizing 3D triangulations using discrete curvature
analysis, in: Mathematical Methods for Curves and Surfaces: Oslo 2000, eds. T. Lyche and
L.L. Schumaker (Vanderbilt Univ. Press, Nashville, TN, 2000) pp. 135–146.
[8] T. Gutzmer and A. Iske, Detection of discontinuities in scattered data approximation, Numer. Algorithms
16 (1997) 155–170.
[9] D. Lei, Robust and efficient algorithms for 1 and ∞ approximations, Ph.D. thesis, School of Computing
and Engineering, University of Huddersfield, Huddersfield, UK (2002).
[10] R. Morandi and A. Sestini, Geometric knot selection for radial scattered data approximation,
in: Algorithms for Approximation IV, eds. J. Levesley, J.C. Mason and I.J. Anderson (Univ. of
Huddersfield, Huddersfield, UK, 2002) pp. 244–251.
[11] M.C. Parra, M.C. López de Silanes and J.J. Torrens, Verticle fault detection from scattered data,
J. Comput. Appl. Math. 73 (1996) 225–239.
[12] J.C. Platt, Fast training of support vector machines using sequential minimal optimization, in:
Advances in Kernel Methods — Support Vector Learning, eds. B. Schölkopf, C.J.C. Burges and
A.J. Smola (MIT Press, Cambridge, MA, 1999) pp. 185–208.
[13] A.J. Smola, B. Schölkopf and G. Rätsch, Linear programs for automatic accuracy control in regression,
in: Ninth International Conference on Artificial Neural Networks, Conference Publications
No. 470 (IEE, London, UK, 1999) pp. 575–580.
[14] R.J. Vanderbei, LOQO: An interior point code for quadratic programming, Technical Report
SOR-94-15, Statistics and Operations Research, Princeton University, Princeton, NJ (1994); revised
(1998).
[15] R.J. Vanderbei and D.F. Shanno, An interior-point algorithm for non-convex nonlinear programming,
Technical Report SOR-97-21, Statistics and Operations Research, Princeton University, Princeton, NJ
(1997).
[16] V.N. Vapnik, Statistical Learning Theory (Wiley, New York, 1998).
[17] G.R. Walsh, An Introduction to Linear Programming, 2nd ed. (Wiley, New York, 1985).