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Park J, Kang D, Kim J, Kwok JT, Tsang IW (2007) SVDD-based pattern denoising. Neural Comput 19:1919-38 [PubMed]

References and models cited by this paper

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Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering J Mach Learn Res 2:125-137

Burges CJC (1999) Geometry and invariance in kernel based methods Advances in kernel methods support vector learning, Smola AJ:Bartlett PL:Scholkopf B:Schuurmons D, ed.

Campbell C, Bennett KP (2001) A linear programming approach to novelty detection Advances in neural information processing systems, Leen TK:Dietterich TG:Tresp V, ed. pp.395

Cox TF, Cox MA (2000) Multidimensional scaling (2nd ed)

Crammer K, Chechik G (2004) A needle in a haystack: Local one-class optimization Proc 21st Intl Conf Mach Learn

Cristianini N, Shawe-taylor J (2000) An introduction to support vector machines

Donoho DL (1995) De-noising by soft thresholding IEEE Trans Inform Theory 41:613-627

Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage J Am Stat Assoc 90:1200-1224

Gower JC (1968) Adding a point to vector diagrams in multivariate analysis Biometrika 55:582-585

Kwok JT, Tsang IW (2004) The pre-image problem in kernel methods. IEEE Trans Neural Netw 15:1517-25 [Journal] [PubMed]

Lanckriet GRG, El_Ghaoui L, Jordan MI (2003) Robust novelty detection with single-class MPM Advances in neural information processing systems, Becker S:Thrun S:Obermayer K, ed. pp.905

Laskov P (2004) Intrusion detection in unlabeled data with quarter-sphere support vector machines Proc Detection of Intrusions and Malware and Vulnerability Assessment :71-82

Mallat SG (1999) A wavelet tour of signal processing (2nd ed)

Mika S, Scholkopf B, Smola AJ, Muller KR, Scholz M, Ratsch G (1999) Kernel PCA and de-noising in feature space Advances in neural information processing systems, Kearns MS:Solla S:Cohn D, ed. pp.536

Moon TK, Stirling WC (2000) Mathematical methods and algorithms for signal processing

Müller KR, Mika S, Rätsch G, Tsuda K, Schölkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12:181-201 [Journal] [PubMed]

Pekalska E, Tax DMJ, Duin RPW (2003) One-class LP classifiers for dissimilarity representations Advances in neural information processing systems, Becker S:Thrun S:Obermayer K, ed. pp.761

Ratsch G, Mika S, Scholkopf B, Muller KR (2002) Constructing Boosting algorithms from SVMs: An application to one-class classification IEEE PAMI 24:1184-1199

Schölkopf B, Mika S, Burges CC, Knirsch P, Müller KR, Rätsch G, Smola AJ (1999) Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw 10:1000-17 [Journal] [PubMed]

Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443-71 [Journal] [PubMed]

Scholkopf B, Platt JC, Smola AJ (2000) Kernel method for percentile feature extraction (Tech. Rep. MSR-TR-2000-22)

Scholkopf B, Smola AJ (2001) Learning with kernels: Support vector machines, regularization, optimization, and beyond

Tax D (2001) One-class classification Unpublished doctoral-dissertation, Delft University of Technology

Tax D, Duin R (1999) Support vector data description Pattern Recognition Letters 20:1191-1199

Williams CKI (2002) On a connection between kernel PCA and metric multidimensional scaling Mach Learn 46:11-19

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