-
5
-
-
0002400882
-
Simplified support vector decision rules
-
L. Saitta, editor, Bari, Italy, Morgan Kaufmann
-
C. J. C. Burges. Simplified support vector decision rules. In L. Saitta, editor, Proceedings of the Thirteenth International Conference on Machine Learning, pages 71-77, Bari, Italy, 1996. Morgan Kaufmann.
-
(1996)
Proceedings of the Thirteenth International Conference on Machine Learning
, pp. 71-77
-
-
Burges, C.J.C.1
-
6
-
-
84898957872
-
Improving the accuracy and speed of support vector machines
-
M. C. Mozer, M. I. Jordan, and T. Petsche, editors, MIT Press
-
C. J. C. Burges and B. Schölkopf. Improving the accuracy and speed of support vector machines. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 375-381. MIT Press, 1997.
-
(1997)
Advances in Neural Information Processing Systems
, vol.9
, pp. 375-381
-
-
Burges, C.J.C.1
Schölkopf, B.2
-
7
-
-
0004134624
-
-
Technical Report 479, Department of Statistics, Stanford University
-
S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. Technical Report 479, Department of Statistics, Stanford University, 1995.
-
(1995)
Atomic Decomposition by Basis Pursuit
-
-
Chen, S.1
Donoho, D.L.2
Saunders, M.A.3
-
9
-
-
0002432565
-
Multivariate adaptive regression splines
-
J. H. Friedman. Multivariate adaptive regression splines. Annals of Statistics, 19(1):1-141, 1991.
-
(1991)
Annals of Statistics
, vol.19
, Issue.1
, pp. 1-141
-
-
Friedman, J.H.1
-
10
-
-
0004123838
-
Least absolute shrinkage is equivalent to quadratic penalisation
-
L. Niklasson, M. Boden, and T. Ziemske, editors, Springer
-
Y. Grandvalet. Least absolute shrinkage is equivalent to quadratic penalisation. In L. Niklasson, M. Boden, and T. Ziemske, editors, Proceedings of the Eighth International Conference on Artificial Neural Networks (ICANN98), pages 201-206. Springer, 1998.
-
(1998)
Proceedings of the Eighth International Conference on Artificial Neural Networks (ICANN98)
, pp. 201-206
-
-
Grandvalet, Y.1
-
11
-
-
33749044832
-
Bayesian logistic regression: A variational approach
-
D. Madigan and P. Smyth, editors, Ft Lauderdale, FL
-
T. S. Jaakkola and M. I. Jordan. Bayesian logistic regression: a variational approach. In D. Madigan and P. Smyth, editors, Proceedings of the 1997 Conference on Artificial Intelligence and Statistics, Ft Lauderdale, FL, 1997.
-
(1997)
Proceedings of the 1997 Conference on Artificial Intelligence and Statistics
-
-
Jaakkola, T.S.1
Jordan, M.I.2
-
12
-
-
0034271876
-
The evidence framework applied to support vector machines
-
J. T.-K. K wok. The evidence framework applied to support vector machines. IEEE Transactions on Neural Networks, 11(5):1162-1173, 2000.
-
(2000)
IEEE Transactions on Neural Networks
, vol.11
, Issue.5
, pp. 1162-1173
-
-
Wok, J.T.-K.K.1
-
13
-
-
0001025418
-
Bayesian interpolation
-
D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4(3):415-447, 1992a.
-
(1992)
Neural Computation
, vol.4
, Issue.3
, pp. 415-447
-
-
MacKay, D.J.C.1
-
14
-
-
0000234257
-
The evidence framework applied to classification networks
-
D. J. C. MacKay. The evidence framework applied to classification networks. Neural Computation, 4(5):720-736, 1992b.
-
(1992)
Neural Computation
, vol.4
, Issue.5
, pp. 720-736
-
-
MacKay, D.J.C.1
-
15
-
-
0000335983
-
Bayesian methods for backpropagation networks
-
E. Domany, J. L. van Hemmen, and K. Schulten, editors, chapter 6, Springer
-
D. J. C. MacKay. Bayesian methods for backpropagation networks. In E. Domany, J. L. van Hemmen, and K. Schulten, editors, Models of Neural Networks III, chapter 6, pages 211-254. Springer, 1994.
-
(1994)
Models of Neural Networks III
, pp. 211-254
-
-
MacKay, D.J.C.1
-
16
-
-
0003319647
-
Introduction to Gaussian processes
-
C. M. Bishop, editor, Springer
-
D. J. C. MacKay. Introduction to Gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, pages 133-165. Springer, 1998.
-
(1998)
Neural Networks and Machine Learning
, pp. 133-165
-
-
MacKay, D.J.C.1
-
17
-
-
0000597408
-
Comparison of approximate methods for handling hyperparameters
-
D. J. C. MacKay. Comparison of approximate methods for handling hyperparameters. Neural Computation, 11(5):1035-1068, 1999.
-
(1999)
Neural Computation
, vol.11
, Issue.5
, pp. 1035-1068
-
-
MacKay, D.J.C.1
-
21
-
-
0003243224
-
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
-
A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, MIT Press
-
J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers. MIT Press, 2000.
-
(2000)
Advances in Large Margin Classifiers
-
-
Platt, J.1
-
23
-
-
0342502195
-
Soft margins for AdaBoost
-
G. Ratsch, T. Onoda, and K.-R. Müller. Soft margins for AdaBoost. Machine Learning, 42 (3):287-320, 2001.
-
(2001)
Machine Learning
, vol.42
, Issue.3
, pp. 287-320
-
-
Ratsch, G.1
Onoda, T.2
Müller, K.-R.3
-
27
-
-
0032594954
-
Input space versus feature space in kernel-based methods
-
B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A. J. Smola. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017, 1999b.
-
(1999)
IEEE Transactions on Neural Networks
, vol.10
, Issue.5
, pp. 1000-1017
-
-
Schölkopf, B.1
Mika, S.2
Burges, C.J.C.3
Knirsch, P.4
Müller, K.-R.5
Rätsch, G.6
Smola, A.J.7
-
28
-
-
84898947199
-
Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers
-
S. A. Solla, T. K. Leen, and K.-R. Müller, editors, MIT Press
-
M. Seeger. Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 603-609. MIT Press, 2000.
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
, pp. 603-609
-
-
Seeger, M.1
-
29
-
-
0032098361
-
The connection between regularization operators and support vector kernels
-
A. J. Smola, B. Schölkopf, and K.-R. Müller. The connection between regularization operators and support vector kernels. Neural Networks, 11:637-649, 1998.
-
(1998)
Neural Networks
, vol.11
, pp. 637-649
-
-
Smola, A.J.1
Schölkopf, B.2
Müller, K.-R.3
-
31
-
-
84899032333
-
Probabilistic methods for support vector machines
-
S. A. Solla, T. K. Leen, and K.-R. Müller, editors, MIT Press
-
P. Sollich. Probabilistic methods for support vector machines. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 349-355. MIT Press, 2000.
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
, pp. 349-355
-
-
Sollich, P.1
-
32
-
-
84899032239
-
The Relevance Vector Machine
-
S. A. Solla, T. K. Leen, and K.-R. Müller, editors, MIT Press
-
M. E. Tipping. The Relevance Vector Machine. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 652-658. MIT Press, 2000.
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
, pp. 652-658
-
-
Tipping, M.E.1
-
35
-
-
84887252594
-
Support vector method for function approximation, regression estimation and signal processing
-
M. C. Mozer, M. I. Jordan, and T. Petsche, editors, MIT Press
-
V. N. Vapnik, S. E. Golowich, and A. J. Smola. Support vector method for function approximation, regression estimation and signal processing. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9. MIT Press, 1997.
-
(1997)
Advances in Neural Information Processing Systems
, vol.9
-
-
Vapnik, V.N.1
Golowich, S.E.2
Smola, A.J.3
-
36
-
-
0003017575
-
Prediction with Gaussian processes: From linear regression to linear prediction and beyond
-
M. I. Jordan, editor, MIT Press
-
C. K. I. Williams. Prediction with Gaussian processes: from linear regression to linear prediction and beyond. In M. I. Jordan, editor, Learning in Graphical Models, pages 599-621. MIT Press, 1999.
-
(1999)
Learning in Graphical Models
, pp. 599-621
-
-
Williams, C.K.I.1
-
38
-
-
0000673452
-
Bayesian regularisation and pruning using a Laplace prior
-
P. M. Williams. Bayesian regularisation and pruning using a Laplace prior. Neural Computation, 7(1):117-143, 1995.
-
(1995)
Neural Computation
, vol.7
, Issue.1
, pp. 117-143
-
-
Williams, P.M.1
|