Comparing support vector machines and feedforward neural networks with similar hidden-layer weights

IEEE Transactions on Neural Networks

Volumn 18, Issue 3, 2007, Pages 959-963

  Romero, Enrique ^a   Toppo, Daniel ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b EPFL   (Switzerland)

Author keywords

Feedforward neural networks (FNNs); Sparse models; Support vector machines (SVMs)

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; COMPUTER SIMULATION; FEEDFORWARD NEURAL NETWORKS; MATHEMATICAL MODELS;

DATA SETS; SPARSE MODELS; SUBSET;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COMPUTER SIMULATION; EVALUATION; FEEDBACK SYSTEM; INFORMATION RETRIEVAL; LETTER; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FEEDBACK; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER);

EID: 34248640313     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.891656     Document Type: Article

References (13)

1. C. M. Bishop, Neural Networks for Pattern Recognition. New York: Oxford Univ. Press, 1995.
2. C. L. Blake and C. J. Merz, "UCI repository of machine learning databases," Univ. of California, Irvine, CA, 1998 [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html
3. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," 2002 [Online]. Available: http://www.csie.ntu.edu.tw/ ~cjlin/libsvm
4. S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 302-309, Mar. 1991.
5. S. S. Keerthi, O. Chapelle, and D. DeCoste, "Building support vector machines with reduced classifier complexity," J. Mach. Learn. Res. vol. 7, pp. 1493-1515, 2006.
6. T. Y. Kwok and D. Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," IEEE Trans. Neural Netw., vol. 8, no. 3, pp. 630-645, May 1997.
7. M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function," Neural Netw., vol. 6, pp. 861-867, 1993.
8. J. Park and I. W. Sandberg, "Approximation and radial-basis-function networks," Neural Comput., vol. 5, pp. 305-316, 1993.
9. E. Romero and R. Alquézar, "A sequential algorithm for feed-forward neural networks with optimal coefficients and interacting frequencies," Neurocomput., vol. 69, pp. 1540-1552, 2006.
10. B. Schölkopf, A. J. Smola, R. Williamson, and P. Bartlett, "New support vector algorithms," Neural Comput., vol. 12, pp. 1207-1245, 2000.
11. M. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001.
12. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
13. P. Vincent and Y. Bengio, "Kernel matching pursuit," Mach. Learn. vol. 48, pp. 165-187, 2002.