A modified finite Newton method for fast solution of large scale linear SVMs

Journal of Machine Learning Research

Volumn 6, Issue , 2005, Pages

  Keerthi, S Sathiya ^a   Decoste, Dennis ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Classification; Conjugate gradient; Linear SVMs

Indexed keywords

ALGORITHMS; DATA MINING; DATA PROCESSING; ITERATIVE METHODS; MATRIX ALGEBRA; OPTIMIZATION; TEXT PROCESSING; VECTORS;

CLASSIFICATION; CONJUGATE GRADIENT; FINITE NEWTON METHOD; LINEAR SVM;

DATABASE SYSTEMS;

EID: 21844461582     PISSN: 15337928     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. D. P. Bertsekas. Nonlinear Programming. Athena Scientific, Belmont, Massachussetts, 1999.
2. A. Björck. Numerical Methods for Least Squares Problems. SIAM, Philadelphia, 1996.
3. C. L. Blake and C. J. Merz. UCI repository of machine learning databases. Technical report, University of California, Irvine, 1998. www.ics.uci.edu/ ~mlearn/MLRepository.html.
4. S. Chakrabarti, S. Roy, and M. V. Soundalgekar. Fast and accurate text classification via multiple linear discriminant projections. The VLDB Journal, 12:170-185, 2003.
5. W. Chu and S. S. Keerthi. New approaches to support vector ordinal regression. Technical report, Yahoo! Research Labs, Pasadena, California, USA, 2005.
6. D. DeCoste and K. Wagstaff. Alpha seeding for support vector machines. In Proceedings of the International Conference on Knowledge Discovery and Data Mining, pages 345-359, 2000.
7. A. Frommer and P. Maaß. Fast CG-based methods for Tikhonov-Phillips regularization. SIAM Journal of Scientific Computing, 20(5): 1831-1850, 1999.
8. G. Fung and O. L. Mangasarian. Proximal support vector machine classifiers. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 77-86, 2001.
9. C. W. Hsu and C. J. Lin. A simple decomposition method for support vector machines. Machine Learning, 46:291-314, 2002.
10. T. Joachims. Making large-scale SVM learning practical. In. Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, Massachussetts, 1999.
11. W. C. Kao, K.M. Chung, T. Sun, and C. J. Lin. Decomposition methods for linear support vector machines. Neural Computation, 16:1689-1704, 2004.
12. P. Komarek. Logistic regression for data mining and high-dimensional classification. Ph.d. thesis, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, 2004.
13. O. L. Mangasarian. A finite Newton method for classification. Optimization Methods and Software, 17:913-929, 2002.
14. A. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. Technical report, University of Massachssetts, Amherst, Massachusetts, USA, 1996. www.cs.cmu.edu/~mccallum/bow.
15. C. C. Paige and M. A. Saunders. LSQR: An algorithm for sparse linear equations and sparse least squares,. ACM Transactions on Mathematical Software, 8:43-71, 1982.
16. J. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines. In Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, Massachusetts, 1999.
17. J. Suykens and J. Vandewalle. Least squares support vector machine classifiers. Neural Processing Letters, 9(3):293-300, 1999.
18. J. Zhang, R. Jin, Y. Yang, and A. Hauptmann. Modified logistic regression: An approximation to SVM and its applications in large-scale text categorization. In Twentieth International Conference on Machine Learning, pages 472-479, 2003.
19. T. Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization. The Annals of Statistics, 32:56-85, 2004.