A boundary method for outlier detection based on support vector domain description

Pattern Recognition

Volumn 42, Issue 1, 2009, Pages 77-83

  Guo, S M ^a   Chen, L C ^a   Tsai, J S H ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Outlier detection; Support vector domain description

Indexed keywords

IMAGE CLASSIFICATION; VECTORS;

DECISION BOUNDARIES; OUTLIER DETECTION; SUPPORT VECTOR DOMAIN DESCRIPTION;

DATA PROCESSING;

EID: 51649118270     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.07.003     Document Type: Article

References (10)

1. Hodge VJ., and Austin J. A survey of outlier detection methodologies. Artif Intell Rev 22 (2004) 85-126
2. Roberts S.J. Novelty detection using extreme value statistics. Vision Image Signal Process. IEE Proc. 146 3 (1999) 124-129
3. Parzen E. On estimation of a probability density function and mode. Ann. Math. Stat. 33 (1962) 1065-1076
4. D.M.J. Tax, One-class Classification, Ph.D. Thesis, Delft University of Technology, Delft, 2001.
5. Tax D.M.J., and Duin R.P.W. Support vector domain description. Pattern Recognition Lett. 20 (1999) 1191-1199
6. Tax D.M.J., and Juszczak P. Kernel whitening for one-class classification. Lecture Notes in Computer Science vol. 2388 (2002), Springer, Berlin 40-52
7. Hoffmann H. Kernel PCA for novelty detection. Pattern Recognition 40 3 (2007) 863-874
8. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, Department of Information and Computer Sciences, University of California, Irvine, Available at 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
9. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, 2001. Software available at 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉.
10. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 7 (1997) 1145-1159