-
5
-
-
29144499905
-
Working set selection using second order information for training support vector machines
-
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res., 6:1889-1918, 2005. (Pubitemid 41798130)
-
(2005)
Journal of Machine Learning Research
, vol.6
, pp. 1889-1918
-
-
Fan, R.-E.1
Chen, P.-H.2
Lin, C.-J.3
-
6
-
-
0035789613
-
Proximal support vector machine classifiers
-
New York, NY, USA. ACM
-
G. Fung and O. L. Mangasarian. Proximal support vector machine classifiers. In KDD '01: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 77-86, New York, NY, USA, 2001. ACM.
-
(2001)
KDD '01: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, pp. 77-86
-
-
Fung, G.1
Mangasarian, O.L.2
-
8
-
-
0036158552
-
A simple decomposition method for support vector machines
-
DOI 10.1023/A:1012427100071
-
C.-W. Hsu and C.-J. Lin. A simple decomposition method for support vector machines. Mach. Learn., 46:291-314, 2002. (Pubitemid 34129973)
-
(2002)
Machine Learning
, vol.46
, Issue.1-3
, pp. 291-314
-
-
Hsu, C.-W.1
Lin, C.-J.2
-
9
-
-
79551678947
-
-
C.-W. Hsu and C.-J. Lin. BSVM. http://www.csie.ntu.edu.tw/~cjlin/bsvm/, 2006.
-
(2006)
-
-
Hsu, C.-W.1
Lin, C.-J.2
-
10
-
-
33845423521
-
-
Springer, Berlin
-
T.-M. Huang, V. Kecman, and I. Kopriva. Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning. Springer, Berlin, 2006.
-
(2006)
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning
-
-
Huang, T.-M.1
Kecman, V.2
Kopriva, I.3
-
11
-
-
0037399781
-
Polynomial-time decomposition algorithms for support vector machines
-
D. Hush and C. Scovel. Polynomial-time decomposition algorithms for support vector machines. Mach. Learn., 51:51-71, 2003.
-
(2003)
Mach. Learn.
, vol.51
, pp. 51-71
-
-
Hush, D.1
Scovel, C.2
-
12
-
-
33646392997
-
QP algorithms with guaranteed accuracy and run time for support vector machines
-
D. Hush, P. Kelly, C. Scovel, and I. Steinwart. QP algorithms with guaranteed accuracy and run time for support vector machines. J. Mach. Learn. Res., 7:733-769, 2006.
-
(2006)
J. Mach. Learn. Res.
, vol.7
, pp. 733-769
-
-
Hush, D.1
Kelly, P.2
Scovel, C.3
Steinwart, I.4
-
13
-
-
0002714543
-
Making large-scale SVM learning practical
-
B. Schölkopf, C. Burges, and A. Smola, editors, chapter 11, MIT Press, Cambridge, MA
-
T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in KernelMethods - Support Vector Learning, chapter 11, pages 169-184.MIT Press, Cambridge, MA, 1999.
-
(1999)
Advances in KernelMethods - Support Vector Learning
, pp. 169-184
-
-
Joachims, T.1
-
14
-
-
34547315922
-
Iterative single data algorithm for training kernel machines from huge data sets: Theory and performance
-
L. Wang, editor. Springer Verlag
-
V. Kecman, T.-M. Huang, and M. Vogt. Iterative single data algorithm for training kernel machines from huge data sets: Theory and performance. In L. Wang, editor, Support Vector Machines: Theory and Applications, pages 255-274. Springer Verlag, 2005.
-
(2005)
Support Vector Machines: Theory and Applications
, pp. 255-274
-
-
Kecman, V.1
Huang, T.-M.2
Vogt, M.3
-
15
-
-
84864039082
-
An efficient method for gradient-based adaptation of hyperparameters in SVM models
-
MIT Press, Cambridge, MA
-
S. Keerthi, V. Sindhwani, and O. Chapelle. An efficient method for gradient-based adaptation of hyperparameters in SVM models. In Advances in Neural Information Processing Systems 19, pages 673-680. MIT Press, Cambridge, MA, 2007.
-
(2007)
Advances in Neural Information Processing Systems
, vol.19
, pp. 673-680
-
-
Keerthi, S.1
Sindhwani, V.2
Chapelle, O.3
-
16
-
-
0000545946
-
Improvements to Platt's SMO algorithm for SVM classifier design
-
DOI 10.1162/089976601300014493
-
S. S. Keerthi, S. K. Shevade, C. Battacharyya, and K. R. K. Murthy. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput., 13:637-649, 2001. (Pubitemid 33595014)
-
(2001)
Neural Computation
, vol.13
, Issue.3
, pp. 637-649
-
-
Keerthi, S.S.1
Shevade, S.K.2
Bhattacharyya, C.3
Murthy, K.R.K.4
-
17
-
-
0035506741
-
On the convergence of the decomposition method for support vector machines
-
C. J. Lin. On the convergence of the decomposition method for support vector machines. IEEE Trans. Neural Networks, 12:1288-1298, 2001.
-
(2001)
IEEE Trans. Neural Networks
, vol.12
, pp. 1288-1298
-
-
Lin, C.J.1
-
18
-
-
0036129250
-
Asymptotic convergence of an SMO algorithm without any assumptions
-
C. J. Lin. Asymptotic convergence of an SMO algorithm without any assumptions. IEEE Trans. Neural Networks, 13:248-250, 2002a.
-
(2002)
IEEE Trans. Neural Networks
, vol.13
, pp. 248-250
-
-
Lin, C.J.1
-
19
-
-
0036129250
-
A formal analysis of stopping criteria of decomposition methods for support vector machines
-
C. J. Lin. A formal analysis of stopping criteria of decomposition methods for support vector machines. IEEE Trans. Neural Networks, 13:248-250, 2002b.
-
(2002)
IEEE Trans. Neural Networks
, vol.13
, pp. 248-250
-
-
Lin, C.J.1
-
20
-
-
9444296042
-
A General Convergence Theorem for the Decomposition Method
-
Learning Theory
-
N. List and H.-U. Simon. A general convergence theorem for the decomposition method. In Proceedings of the 17th Annual Conference on Learning Theory, pages 363-377. Springer, Heidelberg, 2004. (Pubitemid 38940346)
-
(2004)
LECTURE NOTES IN COMPUTER SCIENCE.
, Issue.3120
, pp. 363-377
-
-
List, N.1
Simon, H.U.2
-
21
-
-
26944489027
-
General polynomial time decomposition algorithms
-
S. Ben-David, J. Case, and A.Maruko, editors, Springer, Heidelberg
-
N. List and H. U. Simon. General polynomial time decomposition algorithms. In S. Ben-David, J. Case, and A.Maruko, editors, Proceedings of the 18th Annual Conference on Learning Theory, COLT 2005, pages 308-322. Springer, Heidelberg, 2005.
-
(2005)
Proceedings of the 18th Annual Conference on Learning Theory, COLT 2005
, pp. 308-322
-
-
List, N.1
Simon, H.U.2
-
23
-
-
38049009869
-
Gaps in support vector optimization
-
N. Bshouty and C. Gentile, editors. Springer, New York
-
N. List, D. Hush, C. Scovel, and I. Steinwart. Gaps in support vector optimization. In N. Bshouty and C. Gentile, editors, Proceedings of the 20th Conference on Learning Theory, pages 336-348. Springer, New York, 2007.
-
(2007)
Proceedings of the 20th Conference on Learning Theory
, pp. 336-348
-
-
List, N.1
Hush, D.2
Scovel, C.3
Steinwart, I.4
-
24
-
-
0026678659
-
On the convergence of the coordinate descent method for convex differentiable minimization
-
L.Q. Luo and P. Tseng. On the convergence of the coordinate descent method for convex differentiable minimization. J. Optimization Theory Appl., 72:7-35, 1992.
-
(1992)
J. Optimization Theory Appl.
, vol.72
, pp. 7-35
-
-
Luo, L.Q.1
Tseng, P.2
-
26
-
-
4644354708
-
Sparseness of support vector machines
-
I. Steinwart. Sparseness of support vector machines. J. Mach. Learn. Res., 4:1071-1105, 2003.
-
(2003)
J. Mach. Learn. Res.
, vol.4
, pp. 1071-1105
-
-
Steinwart, I.1
-
28
-
-
38049041673
-
An oracle inequality for clipped regularized risk minimizers
-
B. Schölkopf, J. Platt, and T. Hoffman, editors, MIT Press, Cambridge, MA
-
I. Steinwart, D. Hush, and C. Scovel. An oracle inequality for clipped regularized risk minimizers. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 1321-1328. MIT Press, Cambridge, MA, 2007.
-
(2007)
Advances in Neural Information Processing Systems
, vol.19
, pp. 1321-1328
-
-
Steinwart, I.1
Hush, D.2
Scovel, C.3
-
29
-
-
3543134928
-
SMO algorithms for support vector machines without bias
-
M. Vogt. SMO algorithms for support vector machines without bias. Technical report, University of Darmstadt, 2002. http://www.rtm.tu-darmstadt.de/ ehemalige-mitarbeiter/~vogt/docs/vogt-2002-smowob.pdf.
-
(2002)
Technical Report University of Darmstadt
-
-
Vogt, M.1
|