Creation of specific-to-problem kernel functions for function approximatio

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5517 LNCS, Issue PART 1, 2009, Pages 335-342

  Rubio, Ginés ^a   Pomares, Héctor ^a   Rojas, Ignacio ^a   Guillén, Alberto ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COSTS; GENERALIZATION CAPABILITY; KERNEL METHODS; KERNEL SPACE; NON-PARAMETRIC NOISE; PRIOR KNOWLEDGE; PROBLEM KERNEL; RADIAL BASIS FUNCTIONS; REGRESSION MODEL; REGRESSION PROBLEM;

BACKPROPAGATION; GENETIC PROGRAMMING; PARALLEL PROGRAMMING; PARAMETER ESTIMATION; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

NEURAL NETWORKS;

EID: 68749111852     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02478-8_42     Document Type: Conference Paper

References (17)

1. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing, Singapore (2002)
2. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (2005)
3. Rubio, G., Guillen, A., Herrera, L.J., Pomares, H., Rojas, I.: Use of specific-to-problem kernel functions for time series modeling. In: ESTSP 2008: Proceedings of the European Symposium on Time Series Prediction, pp. 177-186 (2008)
4. Koza, J.: Genetic Programming. MIT Press, Cambridge (1992)
5. Rubio, G., Pomares, H., Rojas, I., Guillén, A.: A basic approach to reduce the complexity of a self-generated fuzzy rule-table for function approximation by use of symbolic regression in 1d and 2d cases. In: IWINAC (2), pp. 143-152 (2005)
6. Rubio, G., Pomares, H.: A basic approach to reduce the complexity of a self-generated fuzzy rule-table for function approximation by use of symbolic interpolation. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 34-41. Springer, Heidelberg (2005)
7. Howley, T., Madden, M.: The genetic kernel support vector machine: Description and evaluation. Artificial Intelligence Review 24, 379-395 (2005)
8. Gagné, C., Schoenauer, M., Sebag, M., Tomassini, M.: Genetic programming for kernel-based learning with co-evolving subsets selection. DANS PPSN 06, 1008 (2006)
9. Methasate, I., Theeramunkong, T.: Kernel Trees for Support Vector Machines. IEICE Trans. Inf. Syst. E90-D(10), 1550-1556 (2007)
10. Sullivan, K.M., Luke, S.: Evolving kernels for support vector machine classification. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1702-1707. ACM Press, New York (2007)
11. Diosan, L., Rogozan, A., Pecuchet, J.P.: Evolving kernel functions for svms by genetic programming. In: ICMLA 2007: Proceedings of the Sixth International Conference on Machine Learning and Applications, Washington, DC, USA, pp. 19-24. IEEE Computer Society Press, Los Alamitos (2007)
12. Lendasse, A., Corona, F., Hao, J., Reyhani, N., Verleysenp, M.: Determination of the mahalanobis matrix using nonparametric noise estimations. In: ESANN, pp. 227-232 (2006)
13. Koza, J.R.: A genetic approach to the truck backer upper problem and the intertwined spiral problem. In: Proceedings of IJCNN International Joint Conference on Neural Networks, vol. IV, pp. 310-318. IEEE Press, Los Alamitos (1992)
14. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)
15. Mladenović, N., Hansen, P.: Variable neighborhood search. Comps. in Opns. Res. 24, 1097-1100 (1997)
16. Guillén, A.: MATLAB Interface for Sun MPI functions, http://atc.ugr.es/~aguillen/MPI/MPI-MATLAB.htm
17. Guillen, A., Rojas, I., Rubio, G., Pomares, H., Herrera, L.J., Gonzalez, J.: A new interface for mpi in matlab and its application over a genetic algorithm. In: ESTSP 2008: Proceedings of the European Symposium on Time Series Prediction, pp. 37-46 (2008)