On problem-oriented kernel refining

Neurocomputing

Volumn 55, Issue 1-2, 2003, Pages 135-150

  Parrado Hernández, Emi ^a   Arenas Garcia J ^a   Mora Jimenez I ^a   Navia Vazquez A ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Compact architectures; Growing; Kernel design; Support vector machine

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; ITERATIVE METHODS; PROBLEM SOLVING;

KERNEL METHODS;

LEARNING SYSTEMS;

ACCURACY; ARTICLE; CLASSIFICATION; CONTROLLED STUDY; DATA BASE; LEARNING; MATHEMATICAL ANALYSIS; METHODOLOGY; PRIORITY JOURNAL; PROBLEM SOLVING; VALIDATION PROCESS;

EID: 0242351912     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(03)00432-6     Document Type: Article

References (18)

1. D.P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, MA, 1999.
2. C.L. Blake, C.J. Merz, UCI Repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA, 1998. [http://www.ics.uci.edu/~mlearn/ MLRepository.html].
3. C.J.C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery 2 (1999) 121-167.
4. T. Jaakkola, M. Diekhans, D. Haussler, Using the Fisher kernel method to detect remote protein homologies, in: Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, Menlo Park, CA, 1999.
5. T. Joachims, Making large-scale SVM learning practical, in: B. Schölkopf, C. Burges, A. Smola (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA, 1999, pp. 169-184.
6. J.T. Kwok, Moderating the output of support vector machine classifiers, IEEE Trans. Neural Networks 10 (5) (1999) 1018-1031.
7. Y.-J. Lee, O.L. Mangasarian, RSVM: Reduced Support Vector Machines, in: Proceedings of the SIAM International Conference on Data Mining, Chicago, IL, 2001.
8. K.-M. Lin, C.-J. Lin, A study on reduced support vector machines, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, February 2002.
9. H. Lodhi, J. Shawe-Taylor, N. Cristianini, C.J.C.H. Watkins, Text classification using string kernels, in: T.K. Leen, T.G. Dietterich, V. Tresp (Eds.), Advances in Neural Information Processing Systems, Vol. 13, MIT Press, Cambridge, MA, 2001, pp. 563-569.
10. A. Navia-Vázquez, F. Pérez-Cruz, A. Artés-Rodríguez, A.R. Figueiras-Vidal, Weighted least squares training of support vector classifiers leading to compact and adaptive schemes, IEEE Trans. Neural Networks 12 (5) (2001) 1047-1059.
11. E. Parrado-Hernández, I. Mora-Jiménez, J. Arenas-Garcia, A.R. Figueiras-Vidal, A. Navia-Vázquez, Growing support vector classifiers with controlled complexity, Pattern Recognition 36 (2003) 1479-1488.
12. E. Parrado-Hernández, I. Mora-Jiménez, A. Navia-Vázquez, Growing support vector classifiers via architecture boosting, in: Proceedings of the Learning'00, Madrid, Spain, 2000.
13. F. Pérez-Cruz, P. Alarcón-Diana, A. Navia-Vázquez, A. Artés-Rodríguez, Fast training of support vector classifiers, in: T.K. Leen, T.G. Dietterich, V. Tresp (Eds.), Advances in Neural Information Processing Systems, Vol. 13, MIT Press, Cambridge, MA, 2001, pp. 724-740.
14. A. Ruiz, P.E. López-de-Teruel, Nonlinear kernel-based statistical pattern analysis, IEEE Trans. Neural Networks 12 (1) (2001) 16-32.
15. B. Schölkopf, A.J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.
16. B. Schölkopf, A. Smola, K.-R. Müller, Nonlinear component analysis in a kernel eigenvalue problem, Neural Comput. 10 (1998) 1299-1319.
17. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
18. V. Vapnik, S. Golowich, A. Smola, Support vector method for function approximation, regression estimation, and signal processing, in: M. Mozer, M. Jordan, T. Petsche (Eds.), Advances in Neural Information Processing Systems, Vol. 9, MIT Press, Cambridge, MA, 1997, pp. 281-287.