An optimal sphere and two large margins approach for novelty detection

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Le, Trung ^a   Tran, Dat ^a   Ma, Wanli ^a   Sharma, Dharmendra ^a  

^a UNIVERSITY OF CANBERRA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ABNORMAL DATA; DETECTION PROBLEMS; FALSE POSITIVE RATES; HYPER-SPHERES; IMBALANCED DATASET; LARGE MARGINS; NOVELTY DETECTION; REAL APPLICATIONS; TRAINING DATASET; TRUE POSITIVE;

EID: 79959415640     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596456     Document Type: Conference Paper

References (21)

1. M. M. Moya, M. W. Koch, and L. D. Hostetler, "One-class classifier networks for target recognition applications," in Proceedings of world congress on neural networks, Portland, pp. 797-801.
2. C. Campbell and K.P.Bennet, "A linear programming approach to novelty detection," Advances in neural information processing, 2001.
3. G. G. Towel, "Local expert autoassociator for anomaly detection," In Proc. 17th International Conference on Machine Learning, 2000.
4. V. Bamett and T. Lewis, Outliers in statistical data, 3rd ed. Wiley, 1978.
5. C.M.Bishop, "Novelty detection and neural network validation," IEEE Proceedings-Vision, Image, and Signal Processing, 1994.
6. S. Roberts and L. Tarassenko, "A probabilistic resource allocation network for novelty detection," Neural Computation, vol. 6, p. 270:284, 1994.
7. R. Gunter and G. T. María, "Outliers in statistical pattern recognition and an application to automatic chromosome classification," Pattern Recognition Letters, vol. 18, no. 6, pp. 525-539, Jun. 1997.
8. R.D Michael and L.P. Richard, "Neural Network Classifiers Estimate Bayesian a posteriori Probabilities," Neural computation, MIT Press, 1991.
9. C. M. Bishop, "Neural networks for pattern recognition".
10. B. D. Ripley, Pattern Recognition and Neural Networks . Cambridge university press, 1996.
11. C. D. Scott and R. D. Nowak, "Learning minimum volume sets," Journal of Machine Learning, vol. 7, pp. 665-704, 2006.
12. I. Steinwart, D. Hush, and C.Scovel, "A classification frarnework for anomaly detection ," Journal of Machine Learning Research, vol. 6, pp. 211-232, 2005.
13. J. Vert and J.P. Vert, "Consistency and convergence rates of one class svm and related algorithm," Journal of Machine Learning Research, vol. 7, pp. 817-854, 2006.
14. Schölkopf and A. J. Smola, Learning with kernel. Cambridge: The MIT Press, 2002.
15. D. M. J. Tax and R. P. W. Duin, "Support vector data description," Machine Learning, no. 54, pp. 45-56, 2004.
16. M. Yu and J. Ye , "A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers ," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 31, pp. 2088-2092, Nov. 2009.
17. Y. Lin, Y. Lee, and G. Wahba, "Support vector machine for classification in nonstandard situations ," Machine Learning, vol. 15, pp. 1115-1148, 2002.
18. C.-C. Chang and C.-J. Lin, "LIBSVM: A Library for Support Vector Machines," the LIBSVM software package is available at http://www.csie.ntu. edu.tw/~cjlinlibsvm. 2001.
19. M. Kubat and S. Matwin, "Addressing the curse of imbalanced training set: One-sided selection," in Proc. 14th International Conference on Machine Learning, 1997.
20. V. Vapnik, The nature of statistic learning theory. New York: Springer Verlag, 1995.
21. V. Vapnik and O. Chapelle, "Bound on error expectation for support vector machines," Neural Computation, Dec. 2002.