-
1
-
-
0026966646
-
A training algorithm for optimal margin classifiers
-
D. Haussler, ed, pp
-
B.E. Boser, I.M. Guyon, and V.N. Vapnik, "A training algorithm for optimal margin classifiers," Proc. Fifth Ann. ACM Workshop Computational Learning Theory, D. Haussler, ed., pp. 144-152, 1992.
-
(1992)
Proc. Fifth Ann. ACM Workshop Computational Learning Theory
, pp. 144-152
-
-
Boser, B.E.1
Guyon, I.M.2
Vapnik, V.N.3
-
2
-
-
0005396750
-
Automatic capacity tuning of very large VC-dimension classifiers
-
Systems, S. J. Hanson, J. D. Cowan, and C. L. Giles, Eds. San Mateo, CA: Morgan Kaufmann
-
I. Guyon, B. Boser, and V. Vapnik, "Automatic capacity tuning of very large VC-dimension classifiers," in Advances in Neural Information Processing Systems, S. J. Hanson, J. D. Cowan, and C. L. Giles, Eds. San Mateo, CA: Morgan Kaufmann, 1993, vol. 5, pp. 147-155.
-
(1993)
Advances in Neural Information Processing
, vol.5
, pp. 147-155
-
-
Guyon, I.1
Boser, B.2
Vapnik, V.3
-
5
-
-
84887252594
-
Support vector method for function approximation, regression estimation, and signal processing
-
M. Mozer, M. Jordan, and T. Petsche, Eds. Cambridge, MA: MIT Press
-
V. Vapnik, S. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation, and signal processing," in Advances in Neural Information Processing Systems 9, M. Mozer, M. Jordan, and T. Petsche, Eds. Cambridge, MA: MIT Press, 1997.
-
(1997)
Advances in Neural Information Processing Systems
, vol.9
-
-
Vapnik, V.1
Golowich, S.2
Smola, A.3
-
7
-
-
0032098361
-
The connection between regularization operators and support vector kernels
-
A. J. Smola, B. Scholkopf, and K.-R. Müller, "The connection between regularization operators and support vector kernels," Neural Networks, vol. 11, pp. 637-649, 1998.
-
(1998)
Neural Networks
, vol.11
, pp. 637-649
-
-
Smola, A.J.1
Scholkopf, B.2
Müller, K.-R.3
-
10
-
-
0000874557
-
Theoretical foundations of the potential function method in pattern recognition learning
-
M. A. Aizerman, E. M. Braver", L. I. Rozonoer, "Theoretical foundations of the potential function method in pattern recognition learning," Automation and Remote Control, vol. 25, pp. 821-837,
-
Automation and Remote Control
, vol.25
, pp. 821-837
-
-
Aizerman, M.A.1
Braver", E.M.2
Rozonoer, L.I.3
-
12
-
-
0001500115
-
Functions of positive and negative type and their connection with the theory of integral equation
-
J. Mercer, "Functions of positive and negative type and their connection with the theory of integral equation," Philos. Trans. R. Soc. London, vol. A-209, pp. 415-446, 1909.
-
(1909)
Philos. Trans. R. Soc. London
, vol.A-209
, pp. 415-446
-
-
Mercer, J.1
-
13
-
-
0032594959
-
An overview of statistical learning theory
-
V. N. Vapnik. "An overview of statistical learning theory," IEEE Trans. on Neural Networks, vol. 10, pp. 988-999, 1999.
-
(1999)
IEEE Trans. on Neural Networks
, vol.10
, pp. 988-999
-
-
Vapnik, V.N.1
-
17
-
-
0001300994
-
Solution of incorrectly formulated problems and the regularization method
-
A. N. Tihonov, "Solution of incorrectly formulated problems and the regularization method," Sov. Math. Dokl., vol. 4, pp. 1035-1038, 1963.
-
(1963)
Sov. Math. Dokl
, vol.4
, pp. 1035-1038
-
-
Tihonov, A.N.1
-
18
-
-
0001219859
-
Regularization theory and neural networks architectures
-
F. Girosi, M. Jones, and T. Poggio, "Regularization theory and neural networks architectures," Neural Comput., vol. 7, pp. 219-269, 1995.
-
(1995)
Neural Comput
, vol.7
, pp. 219-269
-
-
Girosi, F.1
Jones, M.2
Poggio, T.3
-
19
-
-
40949145952
-
-
F. Girosi, M. Jones, and T. Poggio. Priors, stabilizers and basis functions: From regularization to radial, tensor and additive splines. AI Memo No: 1430, MIT AI Lab, 1993.
-
F. Girosi, M. Jones, and T. Poggio. Priors, stabilizers and basis functions: From regularization to radial, tensor and additive splines. AI Memo No: 1430, MIT AI Lab, 1993.
-
-
-
-
20
-
-
24044515976
-
On a kernel-based method for pattern recognition, regression, approximation and operator inversion
-
A. J. Smola and B. Schölkopf, "On a kernel-based method for pattern recognition, regression, approximation and operator inversion," Algorithmica, vol. 22, pp. 211-231, 1998.
-
(1998)
Algorithmica
, vol.22
, pp. 211-231
-
-
Smola, A.J.1
Schölkopf, B.2
-
22
-
-
0034419669
-
Regularization networks and support vector machines
-
T. Evgeniou, M. Pontil, and T. Poggio, "Regularization networks and support vector machines," Adv. Comput. Math., vol. 13, pp. 1-50, 2000.
-
(2000)
Adv. Comput. Math
, vol.13
, pp. 1-50
-
-
Evgeniou, T.1
Pontil, M.2
Poggio, T.3
-
28
-
-
84938168609
-
-
D. O. North, An analysis of the factors which determine signal/noise discrimination in pulsed-carrier systems, RCA Lab Rep., PTR-6c, Jun. 1943. Reprinted in Proc. IEEE, 51, pp. 1016-1027, Jul. 1963.
-
D. O. North, "An analysis of the factors which determine signal/noise discrimination in pulsed-carrier systems," RCA Lab Rep., PTR-6c, Jun. 1943. Reprinted in Proc. IEEE, vol. 51, pp. 1016-1027, Jul. 1963.
-
-
-
-
29
-
-
26444580186
-
Matched filtering for generalized stationary processes
-
V. Olshevsky, L. Sakhnovich, "Matched filtering for generalized stationary processes," IEEE Trans. Information Theory, vol. 51, pp. 3308-3313, 2005.
-
(2005)
IEEE Trans. Information Theory
, vol.51
, pp. 3308-3313
-
-
Olshevsky, V.1
Sakhnovich, L.2
-
30
-
-
84937077292
-
An introduction to matched filters
-
G. L. Turin, "An introduction to matched filters," IRE Trans. Iform. Theory, vol. IT-6, pp. 311-329, 1960.
-
(1960)
IRE Trans. Iform. Theory
, vol.IT-6
, pp. 311-329
-
-
Turin, G.L.1
-
34
-
-
84956628443
-
Predicting time series with support vector machines
-
K. R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik, "Predicting time series with support vector machines," in Proc. Int. Conf. Artificial Neural Networks, pp. 999-1004, 1997.
-
(1997)
Proc. Int. Conf. Artificial Neural Networks
, pp. 999-1004
-
-
Müller, K.R.1
Smola, A.J.2
Rätsch, G.3
Schölkopf, B.4
Kohlmorgen, J.5
Vapnik, V.6
-
35
-
-
0037695279
-
-
Singapore: World Scientific
-
J.A.K. Suykens, T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, Least Squares Support Vector Machines. Singapore: World Scientific, 2002.
-
(2002)
Least Squares Support Vector Machines
-
-
Suykens, J.A.K.1
Gestel, T.V.2
Brabanter, J.D.3
Moor, B.D.4
Vandewalle, J.5
-
36
-
-
0036925049
-
Time series prediction via new support vector machines
-
J. Y. Zhu, B. Ren, H. X. Zhang and Z. T. Deng, "Time series prediction via new support vector machines," in Proc. First Intl. Conf. of Machine Learning and Cybernetics, ICMLC, pp. 364-366, 2002.
-
(2002)
Proc. First Intl. Conf. of Machine Learning and Cybernetics, ICMLC
, pp. 364-366
-
-
Zhu, J.Y.1
Ren, B.2
Zhang, H.X.3
Deng, Z.T.4
-
38
-
-
0025490985
-
Networks for approximation and learning
-
T. Poggio and F. Girosi. "Networks for approximation and learning," Proc. IEEE, vol. 78, pp. 1481-1497, 1990.
-
(1990)
Proc. IEEE
, vol.78
, pp. 1481-1497
-
-
Poggio, T.1
Girosi, F.2
-
39
-
-
0035441827
-
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators
-
R. C. Williamson, A. J. Smola, B. Scholkopf, "Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators," IEEE Trans. Information Theory, vol. 47, pp. 2516-2532, 2001.
-
(2001)
IEEE Trans. Information Theory
, vol.47
, pp. 2516-2532
-
-
Williamson, R.C.1
Smola, A.J.2
Scholkopf, B.3
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