Up: SVMTorch: Support Vector Machines
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- 1
-
R. Collobert and S. Bengio.
On the Convergence of SVMTorch, an Algorithm for
Large-Scale Regression Problems.
IDIAP-RR 24, IDIAP, 2000.
Available at ftp://www.idiap.ch/pub/reports/2000/rr00-24.ps.gz.
- 2
-
H. Drucker, C. Burges, L. Kaufman, A. Smola, and V. Vapnik.
Support vector regression machines.
In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in
Neural Information Processing Systems 9, pages 155-161. The MIT
Press, 1997.
- 3
-
G.W. Flake and S. Lawrence.
Efficient SVM regression training with SMO.
Submitted to Machine Learning. Available at http://external.nj.nec.com/homepages/flake/smorch.ps, 2000.
- 4
-
R. Fletcher.
Practical Methods of Optimization.
John Wiley and Sons, Chichester, 1987.
- 5
-
T. Joachims.
Making large-scale support vector machine learning practical.
In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in
Kernel Methods. The MIT Press, 1999.
- 6
-
S. S. Keerthi and E. G. Gilbert.
Convergence of a Generalized SMO Algorithm for SVM
Classifier Design.
Technical Report CD-00-01, Control Division, Dept. of Mechanical and
Production Engineering, National University of Singapore, 2000.
Available at http://guppy.mpe.nus.edu.sg/~mpessk/svm/conv_ml.ps.gz.
- 7
-
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, Control Division, Dept. of Mechanical and
Production Engineering, National University of Singapore, 1999.
To appear in Neural Computation. Available at http://guppy.mpe.nus.edu.sg/~mpessk/smo_mod.ps.gz.
- 8
-
P. Laskov.
An improved decomposition algorithm for regression support vector
machines.
In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in
Neural Information Processing Systems 12. The MIT Press, 2000.
- 9
-
C. Lin.
On the Convergence of the Decomposition Method for Support
Vector Machines.
Technical report, National Taiwan University, 2000.
Available at http://www.csie.ntu.edu.tw/~cjlin/papers/conv.ps.gz.
- 10
-
K.-R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik.
Predicting time series with support vector machines.
In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors,
Artificial Neural Networks - ICANN'97, pages 999-1004. Springer,
1997.
- 11
-
E. Osuna, R. Freund, and F. Girosi.
An improved training algorithm for support vector machines.
In J. Principe, L. Giles, N. Morgan, and E. Wilson, editors, Neural Networks for Signal Processing VII - Proceedings of the 1997
IEEE Workshop, pages 276-285. IEEE Press, New York, 1997.
- 12
-
J. C. Platt.
Fast training of support vector machines using sequential minimal
optimization.
In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in
Kernel Methods. The MIT Press, 1999.
- 13
-
S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy.
Improvements to the SMO algorithm for SVM regression.
IEEE Transaction on Neural Networks, 11(5):1188-1183, 2000.
- 14
-
A. Smola and B. Schölkopf.
A Tutorial on Support Vector Regression.
Technical Report NeuroCOLT NC-TR-98-030, Royal Holloway
College,University of London, UK, 1998.
- 15
-
V. Vapnik.
The Nature of Statistical Learning Theory.
Springer, second edition, 1995.
Journal of Machine Learning Research