The theoretical and experimental status of the n-tuple classifier

Neural Networks

Volumn 11, Issue 1, 1998, Pages 1-14

  Rohwer, Richard ^a   Morciniec, Michal ^a  

^a NONE

Author keywords

Benchmarking; CMAC code; Hamming distance; Kanerva model; n Tuple classifier; Regression network; Review; StatLog

Indexed keywords

BENCHMARKING; DATA STRUCTURES; REGRESSION ANALYSIS;

HAMMING DISTANCE FRAMEWORK; KANERVA MODEL;

NEURAL NETWORKS;

ARTICLE; METHODOLOGY; NERVE CELL NETWORK; PRIORITY JOURNAL; THEORY; VELOCITY;

EID: 0031890971     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(97)00062-2     Document Type: Article

References (31)

1. Aleksander, I. & Stonham, T.J. (1979a). Guide to pattern recognition using random-access memories. Computers and Digital Techniques, 2, 29-40.
2. Aleksander ,I. & Stonham, T.J. (1979b). Guide to pattern recognition using random-access memories. IEE Proceedings on Computers and Digital Techniques (part E), 2(1), 29-40.
3. Aleksander I., Thomas W.V., Bowden P.A. WISARD: a radical step forward in image recognition. Sensor Review. 4:1984;120-124.
4. Alexander, I., & Morton, H. (1995). An Introduction to neural computing. The secrets of the WISARD, Chap. 5. London: Thomson.
5. Allinson, N.M., & Kołcz, A. (1993). Enhanced n-tuple approximators. Weightless Neural Network Workshop (pp. 38-45).
6. Allinson, N.M. & Kołcz, A. (1994a). Application of the CMAC input encoding scheme in the n-tuple approximation network. IEE Proceedings on Computers and Digital Technology, 141(3), 177-183.
7. Allinson, N.M., & Kołcz, A. (1994b). The theory and practice of n-tuple neural networks. In Taylor, J.G. (ed), Adaptive computing and information processing: neural networks. Unicom Publishing.
8. Austin, J. (1994). A Review of RAM based Neural Networks. Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems (pp. 58-66) Turin: IEEE Computer Society Press.
9. Badr, A. (1993). n-tuple classifier for ECG signals. In Allinson, N. (Ed.), Proceedings of the Weightless Neural Network Workshop '93, Computing with Logical Neurons (pp. 29-32).
10. Bledsoe W.W., Bisson C.L. Improved memory matrices for the n-tuple pattern recognition method. IEEE Transactions on Electronic Computers. 11:1962;414-415.
11. Bledsoe, W.W., & Browning, I., Pattern recognition and reading by machine. Proceedings of the Eastern Joint Computer Conference (pp. 232-255).
12. Broomhead D.S., Lowe D. Multi-variable functional interpolation and adaptive networks. Complex Systems. 2:1988;321-355.
13. Flanagan C., Rahman M.A., McQuade E. A model for the behaviour of n-tuple RAM classifiers in noise. Journal of Intelligent Systems. 2:1992;187-224.
14. Gallant, S., & Smith, D., (1987). Random Cells: an idea whose time has come and gone.and come again? In Caudill and Butler (eds), IEEE International Conference on Neural Networks (pp. II-671-II-678), San Diego, CA: IEEE.
15. Kanerva, P. (1988). Sparse distributed memory. Cambridge, MA: MIT Press.
16. Kołcz, A., & Allinson, N. (1994). Euclidian input mapping in a n-tuple approximation network. Proceedings of the Sixth IEEE Digital Signal Processing Workshop (pp. 285-289).
17. Luttrell, S. P. (1992). Gibbs distribution theory of adaptive n-tuple networks. In Aleksander, I., & Taylor, J. (Eds.), Artificial neural networks, 2. (pp. 313-316) New York: Elsevier.
18. MacKay D. Bayesian interpolation. Neural Computation. 4:1992;415-447.
19. Michie, D., Spiegelhalter, D.J., & Taylor, C.C., (Eds) (1994). Machine learning, neural and statistical classification. Englewood Cliffs, NJ: Prentice-Hall.
20. Rohwer, R. (1995). Two Bayesian treatments of the n-tuple recognition method. Proceedings of the IEE 4th International Conference on Artificial Neural Networks (publication 409) (pp. 171-176).
21. Rohwer, R., & Cressy, D. (1989). Phoneme classification by Boolean networks. In Tubach, J.P., and Mariani, J.J. (Eds.), Proceedings of the European Conference on Speech Communication and Technology (pp. 557-560) Paris: CEP, 26-28 Albany St., Edinburgh, UK.
22. Rohwer, R., & Lamb, A. (1993). An exploration of the effect of super large n-tuples on single layer RAMnets. In Allinson, N. (Ed.), Proceedings of the Weightless Neural Network Workshop '93, Computing with Logical Neurons (pp. 33-37).
23. Sixsmith M.J., Tattersall G.D., Rollett J.M. Speech recognition using n-tuple Techniques. British Telecom Technology Journal. 8:1990;50-60.
24. Songcan Y., Guoqing Ch., Jun L. Mulitilayer parallel distributed pattern recognition system model using sparse RAM nets. IEE Proceedings-E. 139(2):1992;144-146.
25. Stonham T.J. Improved Hamming-distance analysis for digital learning networks. Electronic Letters. 13(6):1977;155-156.
26. Sutton, R.S., & Whitehead, S.D. (1993). Online learning with random representations. Tenth International Conference on Machine Learning (ML'93).
27. Tarling R., Rohwer R. Efficient use of training data in the n-tuple recognition method. Electronics Letters. 29(24):1993;2093-2094.
28. Tattersall, G.D., & Johnson, R.D. (1984). Speech recognisers based on n-tuple sampling. Proceedings of the Institute of Acoustics - Spring Conference (pp. 405-413).
29. Tattersall G.D., Foster S., Johnston R.D. Single-layer lookup perceptrons. IEE Proceedings-F. 138:1991;46-54.
30. Ullmann J.R., Kidd P.A. Recognition experiments with typed numerals from envelopes in the mail. Pattern Recognition. 1:1969;273-289.
31. Ullmann J.R. Experiments with the n-tuple method of pattern recognition. IEEE Transactions on Computers. 18(12):1969;1135-1137.