-
2
-
-
6344264442
-
-
Springer, Heidelberg
-
Casillas J., Cordón O., Herrera F., and Magdalena L. Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview, Chapter of Interpretability Issues in Fuzzy Modeling (2004), Springer, Heidelberg
-
(2004)
Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview, Chapter of Interpretability Issues in Fuzzy Modeling
-
-
Casillas, J.1
Cordón, O.2
Herrera, F.3
Magdalena, L.4
-
4
-
-
0032094918
-
Similarity measures in fuzzy rule base simplification
-
Setnes M., Babuška R., and Kaymak U. Similarity measures in fuzzy rule base simplification. IEEE Transactions on Systems Man, and Cybernetics-Part B: Cybernetics 28 3 (1998) 376-386
-
(1998)
IEEE Transactions on Systems Man, and Cybernetics-Part B: Cybernetics
, vol.28
, Issue.3
, pp. 376-386
-
-
Setnes, M.1
Babuška, R.2
Kaymak, U.3
-
7
-
-
4043137356
-
A tutorial on support vector regression
-
Smola A.J., and Schölkopf B. A tutorial on support vector regression. Statistics and Computing 14 3 (2004) 199-222
-
(2004)
Statistics and Computing
, vol.14
, Issue.3
, pp. 199-222
-
-
Smola, A.J.1
Schölkopf, B.2
-
9
-
-
1542333735
-
Support vector learning mechanism for fuzzy rule-based modeling: A new approach
-
Chiang J.H., and Hao P.Y. Support vector learning mechanism for fuzzy rule-based modeling: A new approach. IEEE Transactions on Fuzzy Systems 12 1 (2004) 1-12
-
(2004)
IEEE Transactions on Fuzzy Systems
, vol.12
, Issue.1
, pp. 1-12
-
-
Chiang, J.H.1
Hao, P.Y.2
-
10
-
-
33644990689
-
On support vector regression machines with linguistic interpretation of the kernel matrix
-
Leski J.M. On support vector regression machines with linguistic interpretation of the kernel matrix. Fuzzy Sets and Systems 157 (2006) 1092-1113
-
(2006)
Fuzzy Sets and Systems
, vol.157
, pp. 1092-1113
-
-
Leski, J.M.1
-
11
-
-
35748961989
-
Support vector fuzzy adaptive network in regression analysis
-
Shen J., Syau Y., and Lee E.S. Support vector fuzzy adaptive network in regression analysis. Computers and Mathematics with Applications 54 11-12 (2007) 1353-1366
-
(2007)
Computers and Mathematics with Applications
, vol.54
, Issue.11-12
, pp. 1353-1366
-
-
Shen, J.1
Syau, Y.2
Lee, E.S.3
-
12
-
-
0346250790
-
Practical selection of SVM parameters and noise estimation for SVM regression
-
Cherkassky V., and Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 1 (2004) 113-126
-
(2004)
Neural Networks
, vol.17
, Issue.1
, pp. 113-126
-
-
Cherkassky, V.1
Ma, Y.2
-
13
-
-
0346881149
-
Experimentally optimal v in support vector regression for different noise models and parameters settings
-
Chalimourda A., Schölkopf B., and Smola A.J. Experimentally optimal v in support vector regression for different noise models and parameters settings. Neural Networks 17 1 (2004) 127-141
-
(2004)
Neural Networks
, vol.17
, Issue.1
, pp. 127-141
-
-
Chalimourda, A.1
Schölkopf, B.2
Smola, A.J.3
-
14
-
-
44049093391
-
The connection between regularization operators and support vector kernels
-
Smola A.J., and Schölkopf B. The connection between regularization operators and support vector kernels. Neural Networks 10 (1998) 1445-1454
-
(1998)
Neural Networks
, vol.10
, pp. 1445-1454
-
-
Smola, A.J.1
Schölkopf, B.2
-
15
-
-
0346881149
-
Experimentally optimal v in support vector regression for different noise models and parameters settings
-
Chalimourda A., Schölkopf B., and Smola A.J. Experimentally optimal v in support vector regression for different noise models and parameters settings. Neural Networks 17 1 (2004) 127-141
-
(2004)
Neural Networks
, vol.17
, Issue.1
, pp. 127-141
-
-
Chalimourda, A.1
Schölkopf, B.2
Smola, A.J.3
-
17
-
-
33646511274
-
A bottom-up method for simplifying support vector solutions
-
Nguyen D., and Ho T.B. A bottom-up method for simplifying support vector solutions. IEEE Transactions on Neural Network 17 3 (2006) 792-796
-
(2006)
IEEE Transactions on Neural Network
, vol.17
, Issue.3
, pp. 792-796
-
-
Nguyen, D.1
Ho, T.B.2
-
19
-
-
0026839028
-
Nonlinear control via approximate input-output linearization: The ball and beam example
-
Hauser J., Sastry S., and Kokotović P. Nonlinear control via approximate input-output linearization: The ball and beam example. IEEE Transactions on Automatic Control 37 3 (1992) 392-398
-
(1992)
IEEE Transactions on Automatic Control
, vol.37
, Issue.3
, pp. 392-398
-
-
Hauser, J.1
Sastry, S.2
Kokotović, P.3
|