Taming the curse of dimensionality in kernels and novelty detection

Advances in Soft Computing

Volumn 34, Issue , 2006, Pages 425-438

  Evangelista, Paul F ^a   Embrechts, Mark J ^b   Szymanski, Boleslaw K ^c  

^a UNITED STATES MILITARY ACADEMY   (United States)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^c Rensselaer Polytechnic Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33845454236     PISSN: 16153871     EISSN: 18600794     Source Type: Book Series    
DOI: 10.1007/3-540-31662-0_33     Document Type: Article

References (28)

1. Charu C. Aggarwal and Philip S. Yu. Outlier Detection for High Dimensional Data. Santa Barbara, California, 2001. Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data.
2. Kristin P. Bennett and Colin Campbell. Support Vector Machines: Hype or Hallelujah. 2(2), 2001.
3. Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, and Uri Shaft. When Is "Nearest Neighbor" Meaningful? Lecture Notes in Computer Science, 1540:217-235, 1999.
4. Piero Bonissone, Kai Goebel, and Weizhong Yan. Classifier Fusion using Triangular Norms. Cagliari, Italy, June 2004. Proceedings of Multiple Classifier Systems (MCS) 2004.
5. Leo Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
6. Leo Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
7. Chih Chung Chang and Chih-Jen Lin. LIBSVM: A Library for Support Vector Machines. http://www.scie.ntu.edu.tw/cjlin/libsvm, Accessed 5 September, 2004.
8. Yunqiang Chen, Xiang Zhou, and Thomas S. Huang. One-Class SVM for Learning in Image Retrieval. Thessaloniki, Greece, 2001. Proceedings of IEEE International Conference on Image Processing.
9. William DuMouchel, Wen Hua Ju, Alan F. Karr, Matthius Schonlau, Martin Theus, and Yehuda Vardi. Computer Intrusion: Detecting Masquerades. Statistical Science, 16(1):1-17, 2001.
10. Paul F. Evangelista, Piero Bonissone, Mark J. Embrechts, and Boleslaw K. Szymanski. Fuzzy ROC Curves for the One Class SVM: Application to Intrusion Detection. Montreal, Canada, August 2005. International Joint Conference on Neural Networks.
11. Paul F. Evangelista, Piero Bonissone, Mark J. Embrechts, and Boleslaw K. Szymanski. Unsupervised Fuzzy Ensembles and Their Use in Intrusion Detection. Bruges, Belgium, April 2005. European Symposium on Artificial Neural Networks.
12. Andrew G. Glen, Lawrence M. Leemis, and John H. Drew. Computing the Distribution of the Product of Two Continuous Random Variables. Computational Statistics and Data Analysis, 44(3):451-464, 2004.
13. Tin Kam Ho. The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, 1998.
14. Alexander Hofmann, Timo Horeis, and Bernhard Sick. Feature Selection for Intrusion Detection: An Evolutionary Wrapper Approach. Budapest, Hungary, July 2004. International Joint Conference on Neural Networks.
15. Mario Koppen. The Curse of Dimensionality. (held on the internet), September 4-18 2000. 5th Online World Conference on Soft Computing in Industrial Applications (WSC5).
16. Ludmila I. Kuncheva. 'Fuzzy' vs. 'Non-fuzzy' in Combining Classifiers Designed by Boosting. IEEE Transactions on Fuzzy Systems, 11(3):729-741, 2003.
17. Ludmila I. Kuncheva. That Elusive Diversity in Classifier Ensembles. Mallorca, Spain, 2003. Proceedings of 1st Iberian Conference on Pattern Recognition and Image Analysis.
18. Ludmila I. Kuncheva. Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc., 2004.
19. Ludmila I. Kuncheva and C.J. Whitaker. Measures of Diversity in Classifier Ensembles. Machine Learning, 51:181-207, 2003.
20. Junshui Ma and Simon Perkins. Time-series Novelty Detection Using One-class Support Vector Machines. Portland, Oregon, July 2003. International Joint Conference on Neural Networks.
21. Lance Parsons, Ehtesham Haque, and Huan Liu. Subspace Clustering for High Dimensional Data: A Review. SIGKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining, 2004.
22. Vijay K. Rohatgi and A.K.Md. Ehsanes Saleh. An Introduction to Probability and Statistics. Wiley, second edition, 2001.
23. Bernhard Scholkopf, John C. Platt, John Shawe Taylor, Alex J. Smola, and Robert C. Williamson. Estimating the Support of a High Dimensional Distribution. Neural Computation, 13:1443-1471, 2001.
24. John Shawe-Taylor and Nello Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
25. Salvatore Stolfo and Ke Wang. One Class Training for Masquerade Detection. Florida, 19 November 2003. 3rd IEEE Conference Data Mining Workshop on Data Mining for Computer Security.
26. Alexander Strehl and Joydeep Ghosh. Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research, 3:583-617, December 2002.
27. David M.J. Tax and Robert P.W. Duin. Support Vector Domain Description. Pattern Recognition Letters, 20:1191-1199, 1999.
28. Jiong Yang, Wei Wang, Haixun Wang, and Philip Yu. δ-clusters: Capturing Subspace Correlation in a Large Data Set. pages 517-528. 18th International Conference on Data Engineering, 2004.