-
1
-
-
0002703873
-
Trainable grammars for speech recognition
-
J.J. Wolf and D.H. Klatt, editors, Acoustical Society of America, New York, NY
-
J. Baker. Trainable grammars for speech recognition. In J.J. Wolf and D.H. Klatt, editors, Proceedings of the 97th meeting of the Acoustical Society of America, pages 547-550. Acoustical Society of America, New York, NY, 1979.
-
(1979)
Proceedings of the 97th meeting of the Acoustical Society of America
, pp. 547-550
-
-
Baker, J.1
-
2
-
-
84898992474
-
Exponentiated gradient algorithms for large-margin structured classification
-
L. K. Saul, Y. Weiss, and L. Bottou, editors, Cambridge, MA, MIT Press
-
P. L. Bartlett, M. Collins, B. Taskar, and D. McAllester. Exponentiated gradient algorithms for large-margin structured classification. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 113-120, Cambridge, MA, 2005. MIT Press.
-
(2005)
Advances in Neural Information Processing Systems 17
, pp. 113-120
-
-
Bartlett, P.L.1
Collins, M.2
Taskar, B.3
McAllester, D.4
-
3
-
-
0037403111
-
Mirror descent and nonlinear projected subgradient methods for convex optimization
-
A. Beck and M. Teboulle. Mirror descent and nonlinear projected subgradient methods for convex optimization. Operations Research Letters, 31:167-175, 2003.
-
(2003)
Operations Research Letters
, vol.31
, pp. 167-175
-
-
Beck, A.1
Teboulle, M.2
-
4
-
-
49949144765
-
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
-
L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. U.S.S.R. Computational Mathematics and Mathematical Physics, 7:200-217, 1967.
-
(1967)
U.S.S.R. Computational Mathematics and Mathematical Physics
, vol.7
, pp. 200-217
-
-
Bregman, L.M.1
-
6
-
-
0000732463
-
A limited memory algorithm for bound constrained optimization
-
R.H. Byrd, P. Lu, and J. Nocedal. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific and Statistical Computing, 16(5):1190-1208, 1995.
-
(1995)
SIAM Journal on Scientific and Statistical Computing
, vol.16
, Issue.5
, pp. 1190-1208
-
-
Byrd, R.H.1
Lu, P.2
Nocedal, J.3
-
9
-
-
34249753618
-
Support-vector networks
-
C. Cortes and V. 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.2
-
11
-
-
0010442827
-
On the algorithmic implementation of multiclass kernel-based vector machines
-
K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265-292, 2002.
-
(2002)
Journal of Machine Learning Research
, vol.2
, pp. 265-292
-
-
Crammer, K.1
Singer, Y.2
-
12
-
-
50949109579
-
-
N. Cristianini, C. Campbell, and J. Shawe-Taylor. Multiplicative updatings for support-vector learning. Technical report, NC-TR-98-016, Neuro COLT, Royal Holloway College, 1998.
-
N. Cristianini, C. Campbell, and J. Shawe-Taylor. Multiplicative updatings for support-vector learning. Technical report, NC-TR-98-016, Neuro COLT, Royal Holloway College, 1998.
-
-
-
-
13
-
-
34547969126
-
Exponentiated gradient algorithms for log-linear structured prediction
-
Z. Ghahramani, editor, ACM Press, New York, NY
-
A. Globerson, T. Koo, X. Carreras, and M. Collins. Exponentiated gradient algorithms for log-linear structured prediction. In Z. Ghahramani, editor, Proceedings of the 24th International Conference on Machine Learning, pages 305-312. ACM Press, New York, NY, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning
, pp. 305-312
-
-
Globerson, A.1
Koo, T.2
Carreras, X.3
Collins, M.4
-
14
-
-
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. Journal of Machine Learning Research, 7:733-769, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 733-769
-
-
Hush, D.1
Kelly, P.2
Scovel, C.3
Steinwart, I.4
-
15
-
-
0003255599
-
Probabilistic kernel regression models
-
D. Heckerman and J. Whittaker, editors, Morgan Kaufmann, San Francisco, CA
-
T. Jaakkola and D. Haussler. Probabilistic kernel regression models. In D. Heckerman and J. Whittaker, editors, Proceedings of 7th Workshop on Artificial Intelligence and Statistics. Morgan Kaufmann, San Francisco, CA, 1999.
-
(1999)
Proceedings of 7th Workshop on Artificial Intelligence and Statistics
-
-
Jaakkola, T.1
Haussler, D.2
-
17
-
-
30044437592
-
A fast dual algorithm for kernel logistic regression
-
S.S. Keerthi, K.B. Duan, S.K. Shevade, and A. N. Poo. A fast dual algorithm for kernel logistic regression. Machine Learning, 61:151-165, 2005.
-
(2005)
Machine Learning
, vol.61
, pp. 151-165
-
-
Keerthi, S.S.1
Duan, K.B.2
Shevade, S.K.3
Poo, A.N.4
-
18
-
-
0008815681
-
Exponentiated gradient versus gradient descent for linear predictors
-
J. Kivinen and M. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. Information and Computation, 132(1):1-63, 1997.
-
(1997)
Information and Computation
, vol.132
, Issue.1
, pp. 1-63
-
-
Kivinen, J.1
Warmuth, M.2
-
19
-
-
0035575628
-
Relative loss bounds for multidimensional regression problems
-
J. Kivinen and M. Warmuth. Relative loss bounds for multidimensional regression problems. Machine Learning, 45(3):301-329, 2001.
-
(2001)
Machine Learning
, vol.45
, Issue.3
, pp. 301-329
-
-
Kivinen, J.1
Warmuth, M.2
-
21
-
-
80053344876
-
Structured prediction models via the matrixtree theorem
-
Association for Computational Linguistics
-
T. Koo, A. Globerson, X. Carreras, and M. Collins. Structured prediction models via the matrixtree theorem. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 141-150. Association for Computational Linguistics, 2007.
-
(2007)
Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
, pp. 141-150
-
-
Koo, T.1
Globerson, A.2
Carreras, X.3
Collins, M.4
-
22
-
-
0142192295
-
Conditonal random fields: Probabilistic models for segmenting and labeling sequence data
-
C.E. Brodley and A.P. Danyluk, editors, San Francisco, CA, Morgan Kaufmann
-
J. Lafferty, A. McCallum, and F. Pereira. Conditonal random fields: Probabilistic models for segmenting and labeling sequence data. In C.E. Brodley and A.P. Danyluk, editors, Proceedings of the 18th International Conference on Machine Learning, pages 282-289, San Francisco, CA, 2001. Morgan Kaufmann.
-
(2001)
Proceedings of the 18th International Conference on Machine Learning
, pp. 282-289
-
-
Lafferty, J.1
McCallum, A.2
Pereira, F.3
-
23
-
-
84898999495
-
Boosting and maximum likelihood for exponential models
-
T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, MIT Press, Cambridge, MA
-
G. Lebanon and J. Lafferty. Boosting and maximum likelihood for exponential models. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 447-454. MIT Press, Cambridge, MA, 2002.
-
(2002)
Advances in Neural Information Processing Systems 14
, pp. 447-454
-
-
Lebanon, G.1
Lafferty, J.2
-
24
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
Y. LeCun, L. Bottou, Y. Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.11
, pp. 2278-2324
-
-
LeCun, Y.1
Bottou, L.2
Bengio, Y.Y.3
Haffner, P.4
-
25
-
-
38049009869
-
Gaps in support vector optimization
-
N. List, D. Hush, C. Scovel, and I. Steinwart. Gaps in support vector optimization. In Proceedings of the 20th Conference on Learning Theory, pages 336-348, 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
-
27
-
-
34547972634
-
Dual optimization of conditional probability models
-
Technical report, University of Toronto
-
R. Memisevic. Dual optimization of conditional probability models. Technical report, University of Toronto, 2006.
-
(2006)
-
-
Memisevic, R.1
-
28
-
-
21244446518
-
A comparison of numerical optimizers for logistic regression
-
Technical report, Carnegie Mellon University
-
T. Minka. A comparison of numerical optimizers for logistic regression. Technical report, Carnegie Mellon University, 2003.
-
(2003)
-
-
Minka, T.1
-
30
-
-
0036342213
-
Incremental subgradient methods for nondifferentiable optimization
-
A. Nedic and D. P. Bertsekas. Incremental subgradient methods for nondifferentiable optimization. SIAM Journal on Optimization, 12(1):109-138, 2001.
-
(2001)
SIAM Journal on Optimization
, vol.12
, Issue.1
, pp. 109-138
-
-
Nedic, A.1
Bertsekas, D.P.2
-
31
-
-
0003120218
-
Fast training of support vector machines using sequential minimal optimization
-
B. Schöopf, C. Burges, and A. Smola, editors, MIT Press
-
J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schöopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 41-64. MIT Press, 1998.
-
(1998)
Advances in Kernel Methods - Support Vector Learning
, pp. 41-64
-
-
Platt, J.1
-
33
-
-
34548051389
-
Multiplicative updates for nonnegative quadratic programming
-
F. Sha, Y. Lin, L.K. Saul, and D.D. Lee. Multiplicative updates for nonnegative quadratic programming. Neural Computation, 19(8):2004-2031, 2007.
-
(2007)
Neural Computation
, vol.19
, Issue.8
, pp. 2004-2031
-
-
Sha, F.1
Lin, Y.2
Saul, L.K.3
Lee, D.D.4
-
34
-
-
34547964973
-
Pegasos: Primal estimated sub-gradient solver for SVM
-
Z. Ghahramani, editor, ACM Press, New York, NY
-
S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In Z. Ghahramani, editor, Proceedings of the 24th International Conference on Machine Learning, pages 807-814. ACM Press, New York, NY, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning
, pp. 807-814
-
-
Shalev-Shwartz, S.1
Singer, Y.2
Srebro, N.3
-
35
-
-
84898948585
-
Max margin Markov networks
-
S. Thrun, L. Saul, and B. Schölkopf, editors, MIT Press, Cambridge, MA
-
B. Taskar, C. Guestrin, and D. Koller. Max margin Markov networks. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, pages 25-32. MIT Press, Cambridge, MA, 2004a.
-
(2004)
Advances in Neural Information Processing Systems 16
, pp. 25-32
-
-
Taskar, B.1
Guestrin, C.2
Koller, D.3
-
36
-
-
85117165447
-
Max-margin parsing
-
Association for Computational Linguistics
-
B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning. Max-margin parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1-8. Association for Computational Linguistics, 2004b.
-
(2004)
Proceedings of the Conference on Empirical Methods in Natural Language Processing
, pp. 1-8
-
-
Taskar, B.1
Klein, D.2
Collins, M.3
Koller, D.4
Manning, C.5
-
37
-
-
33745771086
-
Structured prediction, dual extragradient and Bregman projections
-
B. Taskar, S. Lacoste-Julien, and M. Jordan. Structured prediction, dual extragradient and Bregman projections. Journal of Machine Learning Research, pages 1627-1653, 2006.
-
(2006)
Journal of Machine Learning Research
, pp. 1627-1653
-
-
Taskar, B.1
Lacoste-Julien, S.2
Jordan, M.3
-
38
-
-
36849059715
-
A scalable modular convex solver for regularized risk minimization
-
ACM Press, New York, NY, USA
-
C.H. Teo, Q. Le, A. Smola, and S.V.N. Vishwanathan. A scalable modular convex solver for regularized risk minimization. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 727-736. ACM Press, New York, NY, USA, 2007.
-
(2007)
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
, pp. 727-736
-
-
Teo, C.H.1
Le, Q.2
Smola, A.3
Vishwanathan, S.V.N.4
-
39
-
-
14344250451
-
Support vector machine learning for interdependent and structured output spaces
-
C.E. Brodley, editor, ACM, New York, NY
-
I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In C.E. Brodley, editor, Proceedings of the 21st International Conference on Machine Learning, pages 823-830. ACM, New York, NY, 2004.
-
(2004)
Proceedings of the 21st International Conference on Machine Learning
, pp. 823-830
-
-
Tsochantaridis, I.1
Hofmann, T.2
Joachims, T.3
Altun, Y.4
-
40
-
-
33749243756
-
Accelerated training of conditional random fields with stochastic gradient methods
-
W.W. Cohen and A. Moore, editors, ACM Press, New York, NY
-
S.V. N. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. R Murphy. Accelerated training of conditional random fields with stochastic gradient methods. In W.W. Cohen and A. Moore, editors, Proceedings of the 23rd International Conference on Machine Learning, pages 969-976. ACM Press, New York, NY, 2006.
-
(2006)
Proceedings of the 23rd International Conference on Machine Learning
, pp. 969-976
-
-
Vishwanathan, S.V.N.1
Schraudolph, N.N.2
Schmidt, M.W.3
Murphy, K.R.4
-
41
-
-
0036158505
-
On the dual formulation of regularized linear systems with convex risks
-
T. Zhang. On the dual formulation of regularized linear systems with convex risks. Machine Learning, 46:91-129, 2002.
-
(2002)
Machine Learning
, vol.46
, pp. 91-129
-
-
Zhang, T.1
-
42
-
-
0347585601
-
Kernel logistic regression and the import vector machine
-
T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, MIT Press, Cambridge, MA
-
J. Zhu and T. Hastie. Kernel logistic regression and the import vector machine. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 1081-1088. MIT Press, Cambridge, MA, 2001.
-
(2001)
Advances in Neural Information Processing Systems 14
, pp. 1081-1088
-
-
Zhu, J.1
Hastie, T.2
|