Recursive finite Newton algorithm for support vector regression in the primal

Neural Computation

Volumn 19, Issue 4, 2007, Pages 1082-1096

  Liefeng, Bo ^a   Ling, Wang ^a   Licheng, Jiao ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34247216124     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2007.19.4.1082     Document Type: Article

References (20)

1. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121-167.
2. Chapelle, O. (2006). Training a support vector machine in the primal. (MPI-Tech. Rep. no. 147). Tübingen: Max Planck Institute for Biological Cybernetics.
3. Clarke, F. H. (1983). Optimization and nonsmooth analysis. New York: Wiley.
4. Fan, R. E., Chen P. H., & Lin C. J. (2005). Working set selection using second order information for training support vector machines. Journal of Machine Learning Research, 6, 1889-1918.
5. Fung, G., & Mangasarian, O. L. (2003). Finite Newton method for Lagrangian support vector machine classification. Neurocomputing, 55(1-2), 39-55.
6. Hiriart-Urruty, J. B., Strodiot, J. J., & Nguyen V. H. (1984). Generalized Hessian matrix and second-order optimality conditions for problems with CL1 data. Applied Mathematics and Optimization, 11, 43-56.
7. Huber, P. (1981). Robust statistics. New York: Wiley.
8. Joachims, T. (1999). Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, & A. J. Smola (Eds.), Advances in kernel methods - Support vector learning. Cambridge, MA: MIT Press.
9. Keerthi, S. S., Chapelle, O., & DeCoste D. (2006). Building support vector machines with reduced classifier complexity. Journal of Machine Learning Research, 7, 1493-1515.
10. Keerthi, S. S., & DeCoste D. M. (2005). A modified finite Newton method for fast solution of large scale linear SVMS. Journal of Machine Learning Research, 6, 341-361.
11. Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K. (2001). Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation, 13(3), 637-649.
12. Kimeldorf, G. S., & Wahba G. (1970). A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Annals of Mathematical Statistics, 41, 495-502.
13. Madsen, K., & Nielsen H. B. (1990). Finite algorithms for robust linear-regression. BIT, 30(4), 682-699.
14. Mangasarian, O. L. (2002). A finite Newton method for classification. Optimization Methods and Software, 17(5), 913-929.
15. Platt, J. (1999). Sequential minimal optimization: A fast algorithm for training support vector machines. In B. Schölkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in kernel methods - Support vector learning. Cambridge, MA: MIT Press.
16. Rasmussen, C., Neal, R., Hinton G., van Camp D., Ghahramani Z., Kustra, R., & Tibshirani R. (1996). The DELVE manual. Available online at http://www.cs.toronto.edu/~delve.
17. Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., & Murthy, K. R. K. (2000). Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 11(5), 1188-1193.
18. Smola, A. J., & Schölkopf, B. (2004). A tutorial on SVR. Statistics and Computing, 14(3), 199-222.
19. Vapnik, V. (1998). Statistical learning theory, New York: Wiley.
20. Zhang, X. D. (2004). Matrix analysis and application. New York: Springer.