-
1
-
-
14344252374
-
Multiple kernel learning, conic duality, and the SMO algorithm
-
C. E. Brodley, editor, ACM
-
F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In C. E. Brodley, editor, Twenty-first international conference on Machine learning. ACM, 2004.
-
(2004)
Twenty-first International Conference on Machine Learning
-
-
Bach, F.R.1
Lanckriet, G.R.G.2
Jordan, M.I.3
-
3
-
-
12244300139
-
Column-generation boosting methods for mixture of kernels
-
W. Kim, R. Kohavi, J. Gehrke, and W. DuMouchel, editors, ACM
-
J. Bi, T. Zhang, and K. P. Bennett. Column-generation boosting methods for mixture of kernels. In W. Kim, R. Kohavi, J. Gehrke, and W. DuMouchel, editors, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 521-526. ACM, 2004.
-
(2004)
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, pp. 521-526
-
-
Bi, J.1
Zhang, T.2
Bennett, K.P.3
-
5
-
-
0036161011
-
Choosing multiple parameters for support vector machines
-
O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1-3):131-159, 2002.
-
(2002)
Machine Learning
, vol.46
, Issue.1-3
, pp. 131-159
-
-
Chapelle, O.1
Vapnik, V.2
Bousquet, O.3
Mukherjee, S.4
-
6
-
-
34249753618
-
Support-vector networks
-
C. Cortes and V.N. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
-
(1995)
Machine Learning
, vol.20
, Issue.3
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.N.2
-
7
-
-
33747894200
-
The relationship between precision-recall and roc curves
-
University of Wisconsin Madison, January
-
J. Davis and M. Goadrich. The relationship between precision-recall and roc curves. Technical report #1551, University of Wisconsin Madison, January 2006.
-
(2006)
Technical Report
, vol.1551
-
-
Davis, J.1
Goadrich, M.2
-
8
-
-
0345438685
-
Roc graphs: Notes and practical considerations for data mining researchers
-
HP Laboratories, Palo Alto, CA, USA, January
-
T. Fawcett. Roc graphs: Notes and practical considerations for data mining researchers. Technical report hpl-2003-4, HP Laboratories, Palo Alto, CA, USA, January 2003.
-
(2003)
Technical Report
, vol.HPL-2003-4
-
-
Fawcett, T.1
-
10
-
-
84899001870
-
Adaptive scaling for feature selection in SVMs
-
S. Thrun S. Becker and K. Obermayer, editors, Cambridge, MA, MIT Press
-
Y. Grandvalet and S. Canu. Adaptive scaling for feature selection in SVMs. In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 553-560, Cambridge, MA, 2003. MIT Press.
-
(2003)
Advances in Neural Information Processing Systems
, vol.15
, pp. 553-560
-
-
Grandvalet, Y.1
Canu, S.2
-
11
-
-
0027657329
-
Semi-infinite programming: Theory, methods and applications
-
R. Hettich and K. O. Kortanek. Semi-infinite programming: Theory, methods and applications. SIAMReview, 3:380-429, 1993.
-
(1993)
SIAMReview
, vol.3
, pp. 380-429
-
-
Hettich, R.1
Kortanek, K.O.2
-
12
-
-
84957069814
-
Text categorization with support vector machines: Learning with many relevant features
-
C. Nédellec and C. Rouveirol, editors, Lecture Notes in Computer Science, Berlin / Heidelberg, Springer-Verlag
-
T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In C. Nédellec and C. Rouveirol, editors, ECML '98: Proceedings of the 10th European Conference on Machine Learning, Lecture Notes in Computer Science, pages 137-142, Berlin / Heidelberg, 1998. Springer-Verlag.
-
(1998)
ECML '98: Proceedings of the 10th European Conference on Machine Learning
, pp. 137-142
-
-
Joachims, T.1
-
13
-
-
0002714543
-
Making large-scale SVM learning practical
-
B. Schölkopf, C.J.C. Burges, and A.J. Smola, editors, Cambridge, MA, USA, MIT Press
-
T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184, Cambridge, MA, USA, 1999. MIT Press.
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 169-184
-
-
Joachims, T.1
-
14
-
-
8844263749
-
A statistical framework for genomic data fusion
-
G.R.G. Lanckriet, T. De Bie, N. Cristianini, M.I. Jordan, and W.S. Noble. A statistical framework for genomic data fusion. Bioinformatics, 20:2626-2635, 2004.
-
(2004)
Bioinformatics
, vol.20
, pp. 2626-2635
-
-
Lanckriet, G.R.G.1
De Bie, T.2
Cristianini, N.3
Jordan, M.I.4
Noble, W.S.5
-
15
-
-
0036358995
-
The spectrum kernel: A string kernel for SVM protein classification
-
R. B. Altman, A. K. Dunker, L. Hunter, K. Lauderdale, and T. E. Klein, editors, Kaua'i, Hawaii
-
C. Leslie, E. Eskin, and W.S. Noble. The spectrum kernel: A string kernel for SVM protein classification. In R. B. Altman, A. K. Dunker, L. Hunter, K. Lauderdale, and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing, pages 564-575, Kaua'i, Hawaii, 2002.
-
(2002)
Proceedings of the Pacific Symposium on Biocomputing
, pp. 564-575
-
-
Leslie, C.1
Eskin, E.2
Noble, W.S.3
-
16
-
-
31844443024
-
Inexact matching string kernels for protein classification
-
MIT Press series on Computational Molecular Biology, MIT Press
-
C. Leslie, R. Kuang, and E. Eskin. Inexact matching string kernels for protein classification. In Kernel Methods in Computational Biology, MIT Press series on Computational Molecular Biology, pages 95-112. MIT Press, 2004.
-
(2004)
Kernel Methods in Computational Biology
, pp. 95-112
-
-
Leslie, C.1
Kuang, R.2
Eskin, E.3
-
17
-
-
35248862907
-
An introduction to boosting and leveraging
-
S. Mendelson and A. Smola, editors, LNCS, Springer
-
R. Meir and G. Rätsch. An introduction to boosting and leveraging. In S. Mendelson and A. Smola, editors, Proc. of the first Machine Learning Summer School in Canberra, LNCS, pages 119-184. Springer, 2003.
-
(2003)
Proc. of the First Machine Learning Summer School in Canberra
, pp. 119-184
-
-
Meir, R.1
Rätsch, G.2
-
18
-
-
0018079655
-
Basic principles of ROC analysis
-
October
-
C.E. Metz. Basic principles of ROC analysis. Seminars in Nuclear Medicine, VIII(4), October 1978.
-
(1978)
Seminars in Nuclear Medicine
, vol.8
, Issue.4
-
-
Metz, C.E.1
-
19
-
-
84899013191
-
Hyperkernels
-
S. Thrun S. Becker and K. Obermayer, editors, Cambridge, MA, MIT Press
-
C. S. Ong, A. J. Smola, and R. C. Williamson. Hyperkernels. In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, volume 15, pages 478-485, Cambridge, MA, 2003. MIT Press.
-
(2003)
Advances in Neural Information Processing Systems 15
, vol.15
, pp. 478-485
-
-
Ong, C.S.1
Smola, A.J.2
Williamson, R.C.3
-
20
-
-
0003120218
-
Fast training of support vector machines using sequential minimal optimization
-
B. Schölkopf, C.J.C. Burges, and A.J. Smola, editors, Cambridge, MA, USA, MIT Press
-
J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208, Cambridge, MA, USA, 1999. MIT Press.
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 185-208
-
-
Platt, J.1
-
24
-
-
0036643047
-
Sparse regression ensembles in infinite and finite hypothesis spaces
-
G. Rätsch, A. Demiriz, and K. Bennett. Sparse regression ensembles in infinite and finite hypothesis spaces. Machine Learning, 48(1-3): 193-221, 2002.
-
(2002)
Machine Learning
, vol.48
, Issue.1-3
, pp. 193-221
-
-
Rätsch, G.1
Demiriz, A.2
Bennett, K.3
-
25
-
-
33748531692
-
Special issue on new methods for model selection and model combination
-
Special Issue on New Methods for Model Selection and Model Combination. Also NeuroCOLT2 Technical Report NC-TR-2000-085.
-
NeuroCOLT2 Technical Report
, vol.NC-TR-2000-085
-
-
-
26
-
-
29144512262
-
RASE: Recognition of alternatively spliced exons in C. elegans
-
G. Rätsch, S. Sonnenburg, and B. Schölkopf. RASE: Recognition of alternatively spliced exons in C. elegans. Bioinformatics, 21:i369-i377, 2005.
-
(2005)
Bioinformatics
, vol.21
-
-
Rätsch, G.1
Sonnenburg, S.2
Schölkopf, B.3
-
28
-
-
26444467746
-
Learning interpretable SVMs for biological sequence classification
-
S. Miyano, J. P. Mesirov, S. Kasif, S. Istrail, P. A. Pevzner, and M. Waterman, editors, Springer-Verlag Berlin Heidelberg
-
S. Sonnenburg, G. Rätsch, and C. Schäfer. Learning interpretable SVMs for biological sequence classification. In S. Miyano, J. P. Mesirov, S. Kasif, S. Istrail, P. A. Pevzner, and M. Waterman, editors, Research in Computational Molecular Biology, 9th Annual International Conference, RECOMB 2005, volume 3500, pages 389-407. Springer-Verlag Berlin Heidelberg, 2005a.
-
(2005)
Research in Computational Molecular Biology, 9th Annual International Conference, RECOMB 2005
, vol.3500
, pp. 389-407
-
-
Sonnenburg, S.1
Rätsch, G.2
Schäfer, C.3
-
29
-
-
31844439279
-
Large scale genomic sequence SVM classifiers
-
L. D. Raedt and S. Wrobel, editors, New York, NY, USA, ACM Press
-
S. Sonnenburg, G. Rätsch, and B. Schölkopf. Large scale genomic sequence SVM classifiers. In L. D. Raedt and S. Wrobel, editors, ICML '05: Proceedings of the 22nd international conference on Machine learning, pages 849-856, New York, NY, USA, 2005b. ACM Press.
-
(2005)
ICML '05: Proceedings of the 22nd International Conference on Machine Learning
, pp. 849-856
-
-
Sonnenburg, S.1
Rätsch, G.2
Schölkopf, B.3
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