A kernel-based Adaline for function approximation

Intelligent Data Analysis

Volumn 3, Issue 4, 1999, Pages 307-313

  Friess T T ^a   Harrison, R F ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Adaline; Method of potential functions; Neural networks; Perceptron

Indexed keywords

CURVE FITTING; FUNCTIONS; NEURAL NETWORKS; PERSONNEL TRAINING; SPEECH PROCESSING;

ADALINE; FUNCTION APPROXIMATION; HIGH-DIMENSIONAL; NEURAL NETWORK ALGORITHM; NUMERICALLY ROBUST; POTENTIAL FUNCTION; REGULARISATION; TRAINING POINTS;

COST FUNCTIONS;

EID: 84883699040     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-1999-3405     Document Type: Article

References (15)

1. M. Aizerman, E. Braverman, L. Rozonoer, Theoretical foundations of the potential function method in pattern recognition learning, Automations and Remote Control 25 (1964) 821-837.
2. c. Cortes, V. Vapnik, Support vector networks, Machine Learning 20 (1995) 273-297.
3. A. Courant, D. Hilbert, Methods of Mathematical Physics, Wiley, New York, 1951.
4. T-T. Friel3, N. Cristianini, et al. The kernel-Adatron algorithm: a fast and simple learning procedure for support vector machines, in: J. Shavlik (Ed.), Machine Learning: Proceedings of the 15th International Conference, Morgan Kaufmann, San Francisco, CA.
5. T-T. Friel3, R.F. Harrison, Perceptrons in kernel feature spaces, Research Report No. 720, The University of Sheffield, Sheffield, 1998.
6. T-T. FrieI3R.F. Harrison, Support vector neural networks: the kernel Adatron with bias and soft margin, Research Report No. 725, The University of Sheffield, Sheffield, 1998.
7. T-T. Friel3, R.F., Harrison, The kernel adatron with bias unit: analysis of the algorithm, Research Report No. 728, The University of Sheffield, Sheffield, 1998.
8. T-T. Friel, R.F. Harrison, The kernel Adaline: a new algorithm for non-linear signal processing and regression, Research Report No. 731, The University of Sheffield, Sheffield, 1998.
9. J. McClelland, D. Rumelhart, Explorations in Parallel Distributed Processing, MIT Press, Cambridge, MA, 1988.
10. R. Minnick, Linear input logic, IRE Transactions on Electronic Computers 10 (1961) 6-16.
11. S. Muroga, I. Toda, S. Takasu, Theory of majority decision elements, Journal of Franklin Institute 271 (1961) 376-419.
12. F. Rosenblatt, Principles of Neurodynamics, Perceptrons and the Theory of Brain Mechanisms, Spartan Books, Washington DC, 1961.
13. ftp://ftp.cs.cmu.edu/afs/cs.cmu.edulproject/benchlvowel.
14. G. Wahba, Support vector machines, Reproducing kernel Hilbert spaces and the randomized GACV, Technical Report 984, Department of Statistics, University of Wisconsin, Madison, 1998.
15. B. Widrow, M. Hoff, Adaptive Switching Circuits (1960) 96-104.