-
3
-
-
84884605870
-
Combining experimental data and physical simulation models in support vector learning
-
Thessaloniki, Greece
-
Bloch, G., Lauer, F., Colin, G., & Chamaillard, Y. (2007). Combining experimental data and physical simulation models in support vector learning. In Proceedings of the 10th international conference on engineering applications of neural networks (pp. 284-295), Thessaloniki, Greece.
-
(2007)
Proceedings of the 10th International Conference on Engineering Applications of Neural Networks
, pp. 284-295
-
-
Bloch, G.1
Lauer, F.2
Colin, G.3
Chamaillard, Y.4
-
5
-
-
0034419669
-
Regularization networks and support vector machines
-
Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1-50.
-
(2000)
Advances in Computational Mathematics
, vol.13
, pp. 1-50
-
-
Evgeniou, T.1
Pontil, M.2
Poggio, T.3
-
6
-
-
85156193010
-
Knowledge-based support vector machine classifiers
-
MIT Press Cambridge
-
Fung, G., Mangasarian, O. L., & Shavlik, J. W. (2002). Knowledge-based support vector machine classifiers. In S. Becker, S. Thrun, & K. Obermayer (Eds.), NIPS (pp. 521-528). Cambridge: MIT Press.
-
(2002)
NIPS
, pp. 521-528
-
-
Fung, G.1
Mangasarian, O.L.2
Shavlik, J.W.3
Becker, S.4
Thrun, S.5
Obermayer, K.6
-
7
-
-
9444280785
-
Knowledge-based nonlinear kernel classifiers
-
Springer Berlin
-
Fung, G., Mangasarian, O. L., & Shavlik, J. W. (2003). Knowledge-based nonlinear kernel classifiers. In Schölkopf, B. & Warmuth, M. K. (Eds.), Lecture notes in computer science : Vol. 2777. COLT (pp. 102-113). Berlin: Springer.
-
(2003)
COLT Lecture Notes in Computer Science 2777
, pp. 102-113
-
-
Fung, G.1
Mangasarian, O.L.2
Shavlik, J.W.3
Schölkopf, B.4
Warmuth, M.K.5
-
8
-
-
34748830318
-
Residual gas fraction measurement and computation
-
4
-
Giansetti, P., Colin, G., Higelin, P., & Chamaillard, Y. (2007). Residual gas fraction measurement and computation. International Journal of Engine Research, 8(4), 347-364.
-
(2007)
International Journal of Engine Research
, vol.8
, pp. 347-364
-
-
Giansetti, P.1
Colin, G.2
Higelin, P.3
Chamaillard, Y.4
-
9
-
-
0003684449
-
-
Springer Berlin
-
Hastie, T., Tibshirani, R., & Friedman, J. et al. (2001). The elements of statistical learning: data mining, inference, and prediction. Berlin: Springer.
-
(2001)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.3
-
10
-
-
36849084228
-
-
Imagine
-
Imagine (2006). Amesim web site www.amesim.com.
-
(2006)
-
-
-
12
-
-
0030109138
-
Identification of non-linear systems using empirical data and prior knowledge-an optimization approach
-
3
-
Johansen, T. (1996). Identification of non-linear systems using empirical data and prior knowledge-an optimization approach. Automatica, 32(3), 337-356.
-
(1996)
Automatica
, vol.32
, pp. 337-356
-
-
Johansen, T.1
-
13
-
-
36849072775
-
Incorporating prior knowledge in support vector machines for classification: A review
-
to appear
-
Lauer, F., & Bloch, G. (2007, to appear). Incorporating prior knowledge in support vector machines for classification: a review. Neurocomputing.
-
(2007)
Neurocomputing
-
-
Lauer, F.1
Bloch, G.2
-
15
-
-
14644414184
-
Learning a function and its derivative forcing the support vector expansion
-
Lázaro, M., Pérez-Cruz, F., & Artés-Rodriguez, A. (2005a). Learning a function and its derivative forcing the support vector expansion. IEEE Signal Processing Letters, 12, 194-197.
-
(2005)
IEEE Signal Processing Letters
, vol.12
, pp. 194-197
-
-
Lázaro, M.1
Pérez-Cruz, F.2
Artés-Rodriguez, A.3
-
16
-
-
27844606351
-
Support vector regression for the simultaneous learning of a multivariate function and its derivatives
-
Lázaro, M., Santamaria, I., Pérez-Cruz, F., & Artés-Rodriguez, A. (2005b). Support vector regression for the simultaneous learning of a multivariate function and its derivatives. Neurocomputing, 69, 42-61.
-
(2005)
Neurocomputing
, vol.69
, pp. 42-61
-
-
Lázaro, M.1
Santamaria, I.2
Pérez-Cruz, F.3
Artés- Rodriguez, A.4
-
17
-
-
29344474034
-
Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression
-
Pittsburgh, PA, USA
-
Maclin, R., Shavlik, J., Torrey, L., Walker, T., & Wild, E. (2005). Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression. In Proceedings of the 20th national conference on artificial intelligence, Pittsburgh, PA, USA.
-
(2005)
Proceedings of the 20th National Conference on Artificial Intelligence
-
-
MacLin, R.1
Shavlik, J.2
Torrey, L.3
Walker, T.4
Wild, E.5
-
18
-
-
0001777975
-
Generalized support vector machines
-
MIT Press Cambridge
-
Mangasarian, O. (2000). Generalized support vector machines. In A. Smola, P. Bartlett, B. Schölkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers (pp. 135-146). Cambridge: MIT Press.
-
(2000)
Advances in Large Margin Classifiers
, pp. 135-146
-
-
Mangasarian, O.1
Smola, A.2
Bartlett, P.3
Schölkopf, B.4
Schuurmans, D.5
-
19
-
-
0036161035
-
Large scale kernel regression via linear programming
-
1-3
-
Mangasarian, O. L., & Musicant, D. R. (2002). Large scale kernel regression via linear programming. Machine Learning, 46(1-3), 255-269.
-
(2002)
Machine Learning
, vol.46
, pp. 255-269
-
-
Mangasarian, O.L.1
Musicant, D.R.2
-
20
-
-
29344472579
-
Knowledge-based kernel approximation
-
Mangasarian, O. L., Shavlik, J. W., & Wild, E. W. (2004). Knowledge-based kernel approximation. Journal of Machine Learning Research, 5, 1127-1141.
-
(2004)
Journal of Machine Learning Research
, vol.5
, pp. 1127-1141
-
-
Mangasarian, O.L.1
Shavlik, J.W.2
Wild, E.W.3
-
22
-
-
0002941010
-
Support vector machines for dynamic reconstruction of a chaotic system
-
MIT Press Cambridge
-
Mattera, D., & Haykin, S. (1999). Support vector machines for dynamic reconstruction of a chaotic system. In B. Schölkopf, C. J. Burges, & A. J. Smola (Eds.), Advances in kernel methods: support vector learning (pp. 211-241). Cambridge: MIT Press.
-
(1999)
Advances in Kernel Methods: Support Vector Learning
, pp. 211-241
-
-
Mattera, D.1
Haykin, S.2
Schölkopf, B.3
Burges, C.J.4
Smola, A.J.5
-
24
-
-
84956628443
-
Predicting time series with support vector machines
-
Müller, K., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines. In Proceedings of the international conference on artificial neural networks (pp. 999-1004).
-
(1997)
Proceedings of the International Conference on Artificial Neural Networks
, pp. 999-1004
-
-
Müller, K.1
Smola, A.2
Rätsch, G.3
Schölkopf, B.4
Kohlmorgen, J.5
Vapnik, V.6
-
25
-
-
0025399567
-
Identification and control of dynamical systems using neural networks
-
1
-
Narendra, K. S., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
-
(1990)
IEEE Transactions on Neural Networks
, vol.1
, pp. 4-27
-
-
Narendra, K.S.1
Parthasarathy, K.2
-
26
-
-
0004007505
-
-
(Technical Report AIM-1347). Massachusetts Institute of Technology, Cambridge, MA, USA
-
Poggio, T., & Vetter, T. (1992). Recognition and structure from one 2D model view: observations on prototypes, object classes and symmetries (Technical Report AIM-1347). Massachusetts Institute of Technology, Cambridge, MA, USA.
-
(1992)
Recognition and Structure from One 2D Model View: Observations on Prototypes, Object Classes and Symmetries
-
-
Poggio, T.1
Vetter, T.2
-
27
-
-
3543143021
-
SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems
-
8
-
Sánchez-Fernández, M., De Prado-Cumplido, M., Arenas-García, J., & Pérez-Cruz, F. (2004). SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Transactions on Signal Processing, 52(8), 2298-2307.
-
(2004)
IEEE Transactions on Signal Processing
, vol.52
, pp. 2298-2307
-
-
Sánchez-Fernández, M.1
De Prado-Cumplido, M.2
Arenas-García, J.3
Pérez-Cruz, F.4
-
28
-
-
84902142380
-
Incorporating invariances in support vector learning machines
-
Springer Berlin
-
Schölkopf, B., Burges, C., & Vapnik, V. (1996). Incorporating invariances in support vector learning machines. In C. von der Malsburg, W. von Seelen J. C. Vorbrüggen, & B. Sendhoff (Eds.), Lecture notes in computer science : Vol. 1112. ICANN (pp. 47-52). Berlin: Springer.
-
(1996)
ICANN Lecture Notes in Computer Science 1112
, pp. 47-52
-
-
Schölkopf, B.1
Burges, C.2
Vapnik, V.3
Von Der Malsburg, C.4
Von Seelen, W.5
Vorbrüggen, J.C.6
Sendhoff, B.7
-
29
-
-
4043137356
-
A tutorial on support vector regression
-
3
-
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222.
-
(2004)
Statistics and Computing
, vol.14
, pp. 199-222
-
-
Smola, A.J.1
Schölkopf, B.2
-
30
-
-
0032098361
-
The connection between regularization operators and support vector kernels
-
4
-
Smola, A. J., Schölkopf, B., & Müller, K. R. (1998). The connection between regularization operators and support vector kernels. Neural Networks, 11(4), 637-649.
-
(1998)
Neural Networks
, vol.11
, pp. 637-649
-
-
Smola, A.J.1
Schölkopf, B.2
Müller, K.R.3
-
31
-
-
84898946392
-
Semiparametric support vector and linear programming machines
-
MIT Press Cambridge
-
Smola, A. J., Friess, T., & Schölkopf, B. (1999a). Semiparametric support vector and linear programming machines. Advances in neural information processing systems (vol. 11, pp. 585-591). Cambridge: MIT Press.
-
(1999)
Advances in Neural Information Processing Systems
, pp. 585-591
-
-
Smola, A.J.1
Friess, T.2
Schölkopf, B.3
-
32
-
-
0033339941
-
Linear programs for automatic accuracy control in regression
-
Edinburgh, UK
-
Smola, A. J., Schölkopf, B., & Rätsch, G. (1999b). Linear programs for automatic accuracy control in regression. In Proceedings of the 9th international conference on artificial neural networks (vol. 2, pp. 575-580) Edinburgh, UK.
-
(1999)
Proceedings of the 9th International Conference on Artificial Neural Networks
, vol.2
, pp. 575-580
-
-
Smola, A.J.1
Schölkopf, B.2
Rätsch, G.3
-
34
-
-
0002081773
-
Support vector regression with ANOVA decomposition kernels
-
MIT Press Cambridge
-
Stitson, M. O., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C., & Weston, J. (1999). Support vector regression with ANOVA decomposition kernels. In B. Schölkopf, C. J. Burges, & A. J. Smola (Eds.), Advances in kernel methods: support vector learning (pp. 285-291). Cambridge: MIT Press.
-
(1999)
Advances in Kernel Methods: Support Vector Learning
, pp. 285-291
-
-
Stitson, M.O.1
Gammerman, A.2
Vapnik, V.3
Vovk, V.4
Watkins, C.5
Weston, J.6
Schölkopf, B.7
Burges, C.J.8
Smola, A.J.9
-
35
-
-
0036825901
-
Modified support vector machines in financial time series forecasting
-
Tay, F., & Cao, L. (2002). Modified support vector machines in financial time series forecasting. Neurocomputing, 48, 847-861.
-
(2002)
Neurocomputing
, vol.48
, pp. 847-861
-
-
Tay, F.1
Cao, L.2
-
36
-
-
0028529307
-
Knowledge-based artificial neural networks
-
1-2
-
Towell, G. G., & Shavlik, J. W. (1994). Knowledge-based artificial neural networks. Artificial Intelligence, 70(1-2), 119-165.
-
(1994)
Artificial Intelligence
, vol.70
, pp. 119-165
-
-
Towell, G.G.1
Shavlik, J.W.2
-
38
-
-
0000148392
-
Inequality-constrained multivariate smoothing splines with application to the estimation of posterior probabilities
-
397
-
Villalobos, M., & Wahba, G. (1987). Inequality-constrained multivariate smoothing splines with application to the estimation of posterior probabilities. Journal of the American Statistical Association, 82(397), 239-248.
-
(1987)
Journal of the American Statistical Association
, vol.82
, pp. 239-248
-
-
Villalobos, M.1
Wahba, G.2
-
39
-
-
84898971943
-
Kernel dependency estimation
-
Weston, J., Chapelle, O., Elisseeff, A., Scholkopf, B., & Vapnik, V. (2003). Kernel dependency estimation. Advances in neural information processing systems (Vol. 15), pp. 873-880.
-
(2003)
Advances in Neural Information Processing Systems
, vol.15
, pp. 873-880
-
-
Weston, J.1
Chapelle, O.2
Elisseeff, A.3
Scholkopf, B.4
Vapnik, V.5
-
40
-
-
0001873884
-
Support vector density estimation
-
MIT Press Cambridge
-
Weston, J., Gammerman, A., Stitson, M. O., Vapnik, V., Vovk, V., & Watkins, C. (1999). Support vector density estimation. In Schölkopf, B., Burges, C. J. & Smola, A. J. (Eds.), Advances in kernel methods: support vector learning (pp. 293-305). Cambridge: MIT Press.
-
(1999)
Advances in Kernel Methods: Support Vector Learning
, pp. 293-305
-
-
Weston, J.1
Gammerman, A.2
Stitson, M.O.3
Vapnik, V.4
Vovk, V.5
Watkins, C.6
Schölkopf, B.7
Burges, C.J.8
Smola, A.J.9
|