Alpha seeding for support vector machines

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Alpha seeding for support vector machines

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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: 345 - 349

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Dennis DeCoste	Machine Learning Systems Group, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA
Kiri Wagstaff	Department of Computer Science, Cornell University, 4156 Upson Hall, Ithaca, NY

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): 7, Downloads (12 Months): 45, Citation Count: 11

Additional Information:

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.

	1	Christopher J. C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, v.2 n.2, p.121-167, June 1998 [doi>10.1023/A:1009715923555]
	2	N. Cristianini, C. Campbell, and J. Shawe-Taylor. Dynamically adapting kernels in support vector machines. Technical Report NeuroCOLT Technical Report NC-TR-98-017, Royal Holloway College, University of London, May 1998.
	3	Dennis DeCoste and Michael Burl. Distortion-invariant recognition via jittered queries. In Computer Vision and Pattern Recognition (CVPR-2000), June 2000.
	4	Isabelle Guyon. Online SVM application list. (See http://www.clopinet.com/isabelle/Projects/SVM/ applist.html.).
	5	Thorsten Joachims, Making large-scale support vector machine learning practical, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
	6	S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, and K.R.K. Murthy. Improvements to Platt's SMO algorithm for svm classifier design. Technical Report CD-99-14, Dept. of Mechanical and Production Engineering, National University of Singapore, 1999.
	7	John C. Platt, Fast training of support vector machines using sequential minimal optimization, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
	8	B. Sch. olkopf, A. Smola, and K.R. M. uller. Nonlinear component analysis as a kernel eigenvalue problem. Technical report no. 44, Max-Planck-Institut for Biologische Kybernetik, T. ubingen, Dec 1996.
	9	Bernhard Schölkopf , Christopher J. C. Burges , Alexander J. Smola, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
	10	Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
	11	Jason Weston, Leave-One-Out Support Vector Machines, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, p.727-733, July 31-August 06, 1999

CITED BY 11

	Wei-Chun Kao , Kai-Min Chung , Chia-Liang Sun , Chih-Jen Lin, Decomposition methods for linear support vector machines, Neural Computation, v.16 n.8, p.1689-1704, August 2004
	S. S. Keerthi , S. K. Shevade, SMO algorithm for least-squares SVM formulations, Neural Computation, v.15 n.2, p.487-507, February 2003
	Trevor Hastie , Saharon Rosset , Robert Tibshirani , Ji Zhu, The Entire Regularization Path for the Support Vector Machine, The Journal of Machine Learning Research, 5, p.1391-1415, 12/1/2004
	Dennis Decoste , Bernhard Schölkopf, Training Invariant Support Vector Machines, Machine Learning, v.46 n.1-3, p.161-190, 2002
	D. Sculley , Gabriel M. Wachman, Relaxed online SVMs for spam filtering, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands
	Navneet Panda , Edward Y. Chang, Efficient top-k hyperplane query processing for multimedia information retrieval, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA
	Lacey Gunter , Ji Zhu, Efficient Computation and Model Selection for the Support Vector Regression, Neural Computation, v.19 n.6, p.1633-1655, June 2007
	Alain Rakotomamonjy , Francis Bach , Stéphane Canu , Yves Grandvalet, More efficiency in multiple kernel learning, Proceedings of the 24th international conference on Machine learning, p.775-782, June 20-24, 2007, Corvalis, Oregon
	Lihong Zheng , Xiangjian He, A comparison of reduced support vector machines, International Journal of Intelligent Systems Technologies and Applications, v.4 n.3/4, p.301-312, February 2008
	Vojtěch Franc , Pavel Laskov , Klaus-Robert Müller, Stopping conditions for exact computation of leave-one-out error in support vector machines, Proceedings of the 25th international conference on Machine learning, p.328-335, July 05-09, 2008, Helsinki, Finland
	Hieu Trung Huynh , Yonggwan Won, Small Number of Hidden Units for ELM with Two-Stage Linear Model, IEICE - Transactions on Information and Systems, v.E91-D n.4, p.1042-1049, April 2008