Learning Vector Quantisation and the Self Organising Map

A multitude of detailed circuits for artificial neural networks has been suggested. The general modes of their operation, however, are still based on much fewer underlying philosophies.

[1]  J. C. Lassalle,et al.  Results on the impact parameter trigger for the selection of charmed particles , 1988 .

[2]  T. Kohonen Self-organized formation of topographically correct feature maps , 1982 .

[3]  M. Cottrell,et al.  Etude d'un processus d'auto-organisation , 1987 .

[4]  A. LaVigna Nonparametric Classification Using Learning Vector Quantization , 1990 .

[5]  Paul L. Zador,et al.  Asymptotic quantization error of continuous signals and the quantization dimension , 1982, IEEE Trans. Inf. Theory.

[6]  L. Rabiner,et al.  The acoustics, speech, and signal processing society - A historical perspective , 1984, IEEE ASSP Magazine.

[7]  Joel Max,et al.  Quantizing for minimum distortion , 1960, IRE Trans. Inf. Theory.

[8]  Allen Gersho,et al.  Asymptotically optimal block quantization , 1979, IEEE Trans. Inf. Theory.

[9]  Teuvo Kohonen,et al.  The 'neural' phonetic typewriter , 1988, Computer.

[10]  J. Makhoul,et al.  Vector quantization in speech coding , 1985, Proceedings of the IEEE.

[11]  S. P. Luttrell,et al.  Self-organisation: a derivation from first principles of a class of learning algorithms , 1989, International 1989 Joint Conference on Neural Networks.

[12]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[13]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[14]  Allen Gersho,et al.  On the structure of vector quantizers , 1982, IEEE Trans. Inf. Theory.

[15]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[16]  Harry F. Olson,et al.  Phonetic typewriter , 1957 .