References

Bishop, 1991

Bishop, C. M. (1991).
Improving the generalization properties of radial basis function neural networks.
Neural Computation, 3(4):579-588.

Broomhead and Lowe, 1988

Broomhead, D. S. and Lowe, D. (1988).
Multivariable functional interpolation and adaptive networks.
Complex Systems, 2:321-355.

Chang and Lin, 2001

Chang, C.-C. and Lin, C.-J. (2001).
LIBSVM: a library for support vector machines.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Chang and Lippmann, 1993

Chang, E. I. and Lippmann, R. P. (1993).
A boundary hunting radial basis function classifier which allocates centers constructively.
In Advances in Neural Information Processing Systems, volume 5, pages 131-138. Morgan Kaufmann, San Mateo, CA.

Cortes and Vapnik, 1995

Cortes, C. and Vapnik, V. (1995).
Support-vector network.
Machine Learning, 20:273-297.

Cuff and Barton, 1999

Cuff, J. A. and Barton, G. J. (1999).
Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.
Proteins: Struct. Funct. Genet., 34:508-519.

Devijver and Kittler, 1982

Devijver, P. A. and Kittler, J. (1982).
Pattern recognition : a statistical approach.
Prentice Hall.

Ghosh and Nag, 2000

Ghosh, J. and Nag, A. (2000).
An overview of radial basis function networks.
Radial Basis Function Neural Network Theory and Applications, R. J. Howlerr and L. C. Jain (Eds).

Hsu and Lin, 2002

Hsu, C.-W. and Lin, C.-J. (2002).
A comparison of methods for multi-class support vector machines.
IEEE Transactions on Neural Networks, 13(2):415-425.

Hua and Sun, 2001

Hua, S. and Sun, Z. (2001).
A novel method of protein secondary structure prediction with high segment overlap measure: Svm approach.
J. Mol. Biol., 308:397 - 407.

Hwang and Bang, 1997

Hwang, Y. and Bang, S. (1997).
An efficient method to construct a radial basis function neural network classifier.
Neural Networks, 10(8):1495-1503.

Jones, 1999

Jones, D. (1999).
Protein secondary structure prediction based on position-specific scoring matrices.
J. Mol. Biol., 292(2):195-202.

Kecman, 2001

Kecman, V. (2001).
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models.
The MIT Press.

Kim and Park, 2003

Kim, H. and Park, H. (2003).
Protein secondary structure prediction based on an improved support vector machines approach.
Protein Eng., 16(8):553-560.

King and Sternberg, 1996

King, R. D. and Sternberg, M. J. E. (1996).
Identification and application of the concepts important for accurate and reliable protein secondary structure prediction.
Protein Sci., 5(11):2298-2310.

Lowe, 1995

Lowe, D. G. (1995).
Similarity metric learning for a variable-kernel classifier.
Neural Computation, 7:72-85.

Lyhyaoui et al., 1999

Lyhyaoui, A., Martinez, M., Mora, I., Vazquez, M., Sancho, J.-L., and Figueiras-Vidal, A. R. (1999).
Sample selection via clustering to construct support vector-like classifiers.
IEEE Transactions on Neural Networks, 10(6):1474.

Michie et al., 1994

Michie, D., Spiegelhalter, D. J., and Taylor, C. C. (1994).
Machine Learning, Neural and Statistical Classification.
Prentice Hall, Englewood Cliffs, N.J.

Mitchell, 1997

Mitchell, T. M. (1997).
Machine Learning.
McGraw-Hill.

Orr, 1995

Orr, M. J. L. (1995).
Regularisation in the selection of radial basis function centres.

Orr, 1996

Orr, M. J. L. (1996).
Introduction to radial basis function networks.
Technical report, Center for Cognitive Science, University of Edinburgh, UK.

Oyang et al., 2005

Oyang, Y.-J., Hwang, S.-C., Ou, Y.-Y., Chen, C.-Y., and Chen, Z.-W. (2005).
Data classification with radial basis function networks based on a novel kernel density estimation algorithm.
To appear in the January issue of IEEE Transactions on Neural Networks.

Park and Sandberg, 1991

Park, J. and Sandberg, I. W. (1991).
Universal approximation using radial-basis-function networks.
Neural Computation, 3(2):246-257.

Poggio and Girosi, 1989

Poggio, T. and Girosi, F. (1989).
A theory of networks for approximation and learning.
Technical Report A.I. Memo 1140, Massachusetts Institute of Technology, Artificial Intelligence Laboratory and Center for Biological Information Processing, Whitaker College.

Press, 1992

Press, W. H. (1992).
Numerical Recipes in C.
Cambridge University Press, Cambridge, second edition.

Riis and Krogh, 1996

Riis, S. K. and Krogh, A. (1996).
Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.
J. Comput. Biol., 3:163 - 183.

Rost and Sander, 1993

Rost, B. and Sander, C. (1993).
Prediction of protein secondary structure structure at better than 70% accuracy.
J. Mol. Biol., pages 584 - 599.

Tarassenko and Roberts, 1994

Tarassenko, I. and Roberts, S. (1994).
Supervised and unsupervised learning in radial basis function classifiers.
In IEE Proceedings-Vision, Image and Signal Processing, volume 141, pages 210-216.

Tikhonov and Arsenin, 1977

Tikhonov, A. N. and Arsenin, V. Y. (1977).
Solutions of Ill-Posed Problems.
V.H. Winston & Sons, John Wiley & Sons, Washington D.C.

Ward et al., 2003

Ward, J. J., McGuffin, L. J., Buxton, B. F., and Jones, D. T. (2003).
Secondary structure prediction with support vector machines.
Bioinformatics, 19(13):1650-1655.