Combining resilient classifiers

Details Combining resilient classifiers Combining resilient classifiers

2011-01-18

2011-01-18

Sparks, Donald (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

C706S012000, C706S048000, C382S155000, C382S159000

Reexamination Certificate

active

07873583

ABSTRACT:
A classification system is described for resiliently classifying data. In various embodiments, the classification system constructs a combined classifier based on multiple classifiers that are constructed to classify a set of training data. The combined classifier can be constructed in parallel with the multiple classifiers and applied to classify data.

REFERENCES:
patent: 5428778 (1995-06-01), Brookes et al.
patent: 5675710 (1997-10-01), Lewis
patent: 5794236 (1998-08-01), Mehrle
patent: 5832470 (1998-11-01), Morita et al.
patent: 5870735 (1999-02-01), Agrawal et al.
patent: 6134344 (2000-10-01), Burges
patent: 6161130 (2000-12-01), Horvitz et al.
patent: 6192360 (2001-02-01), Dumais et al.
patent: 6233575 (2001-05-01), Agrawal et al.
patent: 6278962 (2001-08-01), Klimasauskas et al.
patent: 6327581 (2001-12-01), Platt
patent: 6427141 (2002-07-01), Barnhill
patent: 6507829 (2003-01-01), Richards et al.
patent: 6553365 (2003-04-01), Summerlin et al.
patent: 6556987 (2003-04-01), Brown et al.
patent: 6728690 (2004-04-01), Meek et al.
patent: 6826576 (2004-11-01), Lulich et al.
patent: 6853365 (2005-02-01), Reid et al.
patent: 2002/0083039 (2002-06-01), Ferrari et al.
patent: 2002/0184169 (2002-12-01), Opitz
patent: 2004/0015462 (2004-01-01), Lienhart et al.
patent: 2004/0111453 (2004-06-01), Harris et al.
patent: 2004/0177110 (2004-09-01), Rounthwaite et al.
patent: 2005/0209785 (2005-09-01), Wells et al.
patent: 2005/0216426 (2005-09-01), Weston et al.
patent: 2006/0074828 (2006-04-01), Heumann et al.
patent: 2006/0112095 (2006-05-01), Xie et al.
patent: 2006/0287848 (2006-12-01), Li et al.
patent: 2008/0126280 (2008-05-01), Liu et al.
patent: 2008/0177680 (2008-07-01), Laxman et al.
“Neural Network Ensembles, Cross Validation, and Active Learning”, Anders Krogh, Jesper Vedelsby, Advances in Neural Information Processing Systems 7, pp. 231-238, MIT Press, Cambridge MA, 1995.
“Support Vector Machines”, Marti Hearst, IEEE Intelligent Systems, Jul./Aug. 1998, pp. 18-25.
“Application of Support Vector Machines to Global Prediction of Nuclear Properties”, John W. Clark, Haochen Li, International Journal of Modern Physics B, Mar. 12, 2006, pp. 1-15.
Barreno, Marco et al., “Can Machine Learning Be Secure,?” Proceedings of the ACM Symposium on Information, Computer and Communication Security, ASIACCS'06, Mar. 21-24, 2006, Taipei, Taiwan, © 2006 ACM. , pp. 1-10.
Burges, Christopher J.C., “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, 2, 121-167, 1998, © 1998 Kluwer Academic Publishers, Boston.
Drucker, Harris et al., “Support Vector Machines for Spam Categorization,” IEEE Transactions on Neural Networks, vol. 10, No. 5, Sep. 1999, © 1999 IEEE, pp. 1048-1054.
U.S. Appl. No. 11/464,761, filed Aug. 15, 2006, Liu et al.
U.S. Appl. No. 11/625,249, filed Jan. 19, 2007, Laxman et al.
Lowd, Daniel and Christopher Meek, “Good Word Attacks on Statistical Spam Filters,” CEAS 2005, http://www.ceas.cc/papers-2005/125.pdf, [last accessed Dec. 27, 2007].
Yao, Yuan et al., “Fingerprint Classification with Combinations of Support Vector Machines,”Proc. Third Int'l Conf. Audio- and Video-Based Biometric Person Authentication, pp. 253-258, 2001.
Akbani, Rehan et al., “Applying Support Vector Machines to Imbalanced Datasets,” ECML 2004, LNAI 3201, © Springer-Verlag Berlin Heidelberg 2004, pp. 39-50.
Bottou, Leon et al., “Comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition,” ICPR, Oct. 1994, 11 pages.
Boyapati, Vijay, “Improving Hierarchical Text Classification Using Unlabeled Data,” SIGIR'02 Tampere, Finland, ACM, pp. 363-364.
Bredensteiner, Erin J. and Kristin P. Bennett, “Multicategory Classification by Support Vector Machines,” Computer Optimization and Applications, 1999, 30 pages.
Cai, Lijuan and Thomas Hofmann, “Hierarchical Document Categorization with Support Vector Machines,” CIKM'04, Washington, D.C., © 2004 ACM, pp. 78-87.
Chen, Hao and Susan Dumais, “Bringing Order to the Web: Automatically Categorizing Search Results,” Proceedings of CHI'00, Human Factors in Computing Systems, 2000, pp. 145-152.
Dumais, Susan and Hao Chen, “Hierarchical Classification of Web Content,” SIGIR 2000, Athens, Greece, © 2000 ACM, pp. 256-263.
Dunning, Ted, “Accurate Methods for the Statistics of Surprise and Coincidence,” Computational Linguistics, vol. 19, No. 1, 1993, © 1993 Association for Computational Linguistics, pp. 61-74.
Forman, George, “An Extensive Empirical Study of Feature Selection Metrics for Text Classification,” Journal of Machine Learning Research 3, 2003, © 2003 Hewlett-Packard, pp. 1289-1305.
Ghani, Rayid, “Using Error-Correcting Codes For Text Classification,” ICML, 2000, pp. 303-310.
Goh, King-Shy et al., “Support Vector Machine Pairwise Classifiers with Error Reduction for Image Classification,” ACM Workshop: Multimedia Information Retrieval, Canada, © ACM 2001.
Granitzer, Michael, “Hierarchical Text Classification using Methods from Machine Learning,” Oct. 27, 2003, Master's Thesis at Graz University of Technology, 104 pages.
Hersh, William et al., “OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research,” SIGIR 1994, pp. 192-201.
Hsu, Chih-Wei and Chih-Jen Lin, “A Comparison of Methods for Multi-class Support Vector Machines,” Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, 2001, 26 pages.
Joachims, Thorsten, “Making Large-Scale SVM Learning Practical,” LS-8 Report 24, Jun. 15, 1998, University of Dortmund, Computer Science Department, 17 pages.
Kuncheva et al., “Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy,” 2003.
Lewis, David D. et al., “RCV1: A New Benchmark Collection for Text Categorization Research,” Journal of Machine Learning Research, 5, 2004, pp. 361-397.
Lewis, David D., “Reuters-21578,” Test Collections, 1 page, WayBackMachine: http://web.archive.org/web/20040604003920/http://www.daviddlewis.com/resources/testcollections/reuters21578/ , Jun. 4, 2004.
Mukkamala, Srinivas et al., “Intrusion Detection: Support Vector Machines and Neural Networks,” IEEE, 2002.
Platt, John C., “Fast Training of Support Vector Machines using Sequential Minimal Optimization,” Advances in Kernel Methods - Support Vector Learning, MIT Press, 1999, pp. 185-208.
Sastry, P. S., “An Introduction to Support Vector Machines,” Published as a Chapter in J.C. Misra (ed), Computing and Information Sciences: Recent Trends, Narosa Publishing House, New Delhi, 2003, pp. 1-44.
Sebastiani, Fabrizio, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, No. 1, Mar. 2002, © 2002 ACM, pp. 1-47.
Sun, Aixin and Ee-Peng Lim, “Hierarchical Text Classification and Evaluation,” ICDM, 2001, pp. 521-528.
Wehenkel, Louis, “Machine Learning Approaches to Power System Security Assessment,” IEEE, 1997.
Yang, Huai-Yuan et al., “Heterogeneous Information Integration in Hierarchical Text Classification,” PAKDD, 2006, pp. 240-249.
Yang, Yiming and Xin Liu, “A re-examination of text categorization methods,” SIGIR'99, Berkley, California, © 1999 ACM, pp. 42-49.
Yang, Yiming, “A Study of Thresholding Strategies for Text Categorization,” SIGIR'01, New Orleans, Louisiana, © 2001 ACM, pp. 137-145.
Yang, Yiming, Jian Zhang and Bryan Kisiel, “A Scalability Analysis of Classifiers in Text Categorization,” SIGIR'03, Toronto, Canada, © 2003 ACM, pp. 96-103.

Affiliated with

Also associated with

Rating

Combining resilient classifiers does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Combining resilient classifiers, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Combining resilient classifiers will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-2623794