Function approximation from noisy data by an incremental RBF network

Pattern Recognition

Volumn 32, Issue 12, 1999, Pages 2081-2083

  Carozza, Menita ^a   Rampone, Salvatore ^a  

^a UNIVERSITY OF SANNIO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTER ARCHITECTURE; FUNCTIONS; KNOWLEDGE BASED SYSTEMS; OPTIMIZATION;

FUNCTION APPROXIMATION; INCREMENTAL ESTIMATOR; NOISY DATA; RADIAL BASIS FUNCTION;

NEURAL NETWORKS;

EID: 0033366312     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(99)00101-6     Document Type: Article

References (5)

1. [1] C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
2. [2] A. Esposito, M. Marinaro, D. Oricchio, S. Scarpetta, in: M. Marinaro, R. Tagliaferri (Eds.), A New Incremental Strategy for Function Approximation by Radial Basis Function Neural Networks, Neural Nets WIRN'98, Springer, London, 1998.
3. [3] Z. Ghahramani, M.I. Jordan, in: J.D. Cowan, M.I. Tesauro, J. Alspector (Eds.), Supervised Learning from Incomplete Data via an EM approach, Advances in Neural Information Processing Systems, vol. 6, Morgan Kaufmann, San Mateo, CA, 1994, pp. 120-127.
4. [4] P. Niyogi, F. Girosi, On the Relationship between generalization error, hypothesis complexity, and sample complexity for radial basis functions, Neural Comput. 8 (1996) 819-842.
5. [5] S. Rampone, Linear codes interpolation from noisy patterns by means of a vector quantization process, Comput. Math. Appl. 30 (11) (1995) 91-106.