Burges CJC. (1999). Geometry and invariance in kernel based methods Advances in kernel methods support vector learning.

Campbell C, Bennett KP. (2001). A linear programming approach to novelty detection Advances in neural information processing systems. 13

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

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

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

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

Duin RPW, Pekalska E, Tax DMJ. (2003). One-class LP classifiers for dissimilarity representations Advances in neural information processing systems. 15

Gower JC. (1968). Adding a point to vector diagrams in multivariate analysis Biometrika. 55

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

Jordan MI, Lanckriet GRG, El_Ghaoui L. (2003). Robust novelty detection with single-class MPM Advances in neural information processing systems. 15

Kwok JT, Tsang IW. (2004). The pre-image problem in kernel methods. IEEE transactions on neural networks. 15 [PubMed]

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

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

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 transactions on neural networks. 12 [PubMed]

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

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

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

Scholz M et al. (1999). Kernel PCA and de-noising in feature space Advances in neural information processing systems. 11

Schölkopf B et al. (1999). Input space versus feature space in kernel-based methods. IEEE transactions on neural networks. 10 [PubMed]

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

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

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

Williams CKI. (2002). On a connection between kernel PCA and metric multidimensional scaling Mach Learn. 46