Data selection for support vector machine classifiers

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Data selection for support vector machine classifiers

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Boston, Massachusetts, United States

Pages: 64 - 70

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Glenn Fung	Computer Sciences Department, University of Wisconsin, 1210 West Dayton Street, Madison, WI
Olvi L. Mangasarian	Computer Sciences Department, University of Wisconsin, 1210 West Dayton Street, Madison, WI

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 12, Downloads (12 Months): 70, Citation Count: 9

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references cited by index terms collaborative colleagues

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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	2	Paul S. Bradley , O. L. Mangasarian, Feature Selection via Concave Minimization and Support Vector Machines, Proceedings of the Fifteenth International Conference on Machine Learning, p.82-90, July 24-27, 1998
	3	P. S. Bradley and O. L. Mangasarian. Massive data discrimination via linear support vector machines. Optimization Methods and Software, 13:1-10, 2000. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98- 03.ps.
	4	P. S. Bradley , O. L. Mangasarian , J. B. Rosen, Parsimonious Least Norm Approximation, Computational Optimization and Applications, v.11 n.1, p.5-21, Oct. 1998 [doi>10.1023/A:1018361916442]
	5	P. S. Bradley , O. L. Mangasarian , W. N. Street, Feature Selection Via Mathematical Programming, INFORMS Journal on Computing, v.10 n.2, p.209-217, February 1998 [doi>10.1287/ijoc.10.2.209]
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	13	O. L. Mangasarian. Arbitrary-norm separating plane. Operations Research Letters, 24:15-23, 1999. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/97- 07r.ps.
	14	O. L. Mangasarian. Generalized support vector machines. In A. Smola, P. Bartlett, B. Sch.olkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 135-146, Cambridge, MA, 2000. MIT Press. ftp://ftp.cs.wisc.edu/math-prog/techreports/98-14.ps.
	15	O. L. Mangasarian and David R. Musicant. Data discrimination via nonlinear generalized support vector machines. Technical Report 99-03, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, March 1999. To appear in: Applications and Algorithms of Complementarity", M. C. Ferris, O. L. Mangasarian and J.-S. Pang, editors, Kluwer Academic Publishers,Boston 2000. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/99- 03.ps.
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CITED BY 9

	Glenn M. Fung , Olvi L. Mangasarian , Alexander J. Smola, Minimal kernel classifiers, The Journal of Machine Learning Research, 3, p.303-321, 3/1/2003
	Glenn Fung , Olvi L. Mangasarian, Proximal support vector machine classifiers, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.77-86, August 26-29, 2001, San Francisco, California
	J. Garcke , M. Griebel , M. Thess, Data mining with sparse grids, Computing, v.67 n.3, p.225-253, November 2001
	W. Nick Street , YongSeog Kim, A streaming ensemble algorithm (SEA) for large-scale classification, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.377-382, August 26-29, 2001, San Francisco, California
	Alexander J. Smola , Bernhard Schölkopf, Bayesian kernel methods, Advanced lectures on machine learning, Springer-Verlag New York, Inc., New York, NY, 2003
	Cor J. Veenman , David M. J. Tax, LESS: A Model-Based Classifier for Sparse Subspaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.27 n.9, p.1496-1500, September 2005
	Hila Becker , Marta Arias, Real-time ranking with concept drift using expert advice, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA
	Yi Peng , Gang Kou , Yong Shi , Zhengxin Chen, A Multi-criteria Convex Quadratic Programming model for credit data analysis, Decision Support Systems, v.44 n.4, p.1016-1030, March, 2008
	Zenglin Xu , Rong Jin , Jieping Ye , Michael R. Lyu , Irwin King, Non-monotonic feature selection, Proceedings of the 26th Annual International Conference on Machine Learning, p.1145-1152, June 14-18, 2009, Montreal, Quebec, Canada