-
1
-
-
0001224048
-
Sparse Bayesian learning and the relevance vector machine
-
Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211-244 (2001)
-
(2001)
Journal of Machine Learning Research
, vol.1
, pp. 211-244
-
-
Tipping, M.E.1
-
3
-
-
33646895070
-
Nonlinear feature selection with the potential support vector machine
-
Guyon, I, Gunn, S, Nikravesh, M, Zadeh, L, eds, Springer, Berlin
-
Hochreiter, S., Obermayer, K.: Nonlinear feature selection with the potential support vector machine. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature extraction, foundations and applications, Springer, Berlin (2005)
-
(2005)
Feature extraction, foundations and applications
-
-
Hochreiter, S.1
Obermayer, K.2
-
8
-
-
37249029776
-
-
Aizerman, M.E., Braverman, Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821-837 (1964)
-
Aizerman, M.E., Braverman, Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821-837 (1964)
-
-
-
-
9
-
-
67649142447
-
From Lasso regression to Feature Vector Machine
-
Li, F., Yang, Y., Xing, E.P.: From Lasso regression to Feature Vector Machine, Advances in Neural Information Processing Systems, 18 (2005)
-
(2005)
Advances in Neural Information Processing Systems
, vol.18
-
-
Li, F.1
Yang, Y.2
Xing, E.P.3
-
10
-
-
84899019663
-
Analysis of sparse bayesian learning
-
Dietterich, T, Becker, S, Ghahramani, Z, eds, MIT Press, Cambridge, MA
-
Faul, A., Tipping, M.E.: Analysis of sparse bayesian learning. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14, pp. 383-389. MIT Press, Cambridge, MA (2002)
-
(2002)
Advances in Neural Information Processing Systems
, vol.14
, pp. 383-389
-
-
Faul, A.1
Tipping, M.E.2
-
11
-
-
84958972434
-
-
Dorffner, G, Bischof, H, Hornik, K, eds, pp
-
Faul, A., Tipping, M.: A variational approach to robust regression, in Artificial Neural Networks. In: Dorffner, G., Bischof, H., Hornik, K. (eds.), pp. 95-202 (2001)
-
(2001)
A variational approach to robust regression, in Artificial Neural Networks
, pp. 95-202
-
-
Faul, A.1
Tipping, M.2
-
12
-
-
37249025005
-
-
Roth, V.: The Generalized LASSO, V. IEEE Transactions on Neural Networks, Dorffner, G. 15(1). (2004)
-
Roth, V.: The Generalized LASSO, V. IEEE Transactions on Neural Networks, Dorffner, G. vol. 15(1). (2004)
-
-
-
-
14
-
-
37248999725
-
-
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression, NEUROCOLT Technical Report NC-TR-98-030, Royal Holloway College, London (1998)
-
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression, NEUROCOLT Technical Report NC-TR-98-030, Royal Holloway College, London (1998)
-
-
-
-
15
-
-
24344458137
-
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy
-
Long, F., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226-1238 (2005)
-
(2005)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.27
, Issue.8
, pp. 1226-1238
-
-
Long, F.1
Ding, C.2
-
16
-
-
37249006161
-
-
Guiasu, Silviu.: Information Theory with Applications. McGraw-Hill, New York (1977)
-
Guiasu, Silviu.: Information Theory with Applications. McGraw-Hill, New York (1977)
-
-
-
-
17
-
-
84858495135
-
-
Tipping, M.E.: Microsoft Corporation, http://research.microsoft.com/MLP/ RVM/
-
-
-
Tipping, M.E.1
|