Improved versions of learning vector quantization

The author introduces a variant of (supervised) learning vector quantization (LVQ) and discusses practical problems associated with the application of the algorithms. The LVQ algorithms work explicitly in the input domain of the primary observation vectors, and their purpose is to approximate the theoretical Bayes decision borders using piecewise linear decision surfaces. This is done by purported optimal placement of the class codebook vectors in signal space. As the classification decision is based on the nearest-neighbor selection among the codebook vectors, its computation is very fast. It has turned out that the differences between the presented algorithms in regard to the remaining discretization error are not significant, and thus the choice of the algorithm may be based on secondary arguments, such as stability in learning, in which respect the variant introduced (LVQ2.1) seems to be superior to the others. A comparative study of several methods applied to speech recognition is included

[1]  Shigeru Katagiri,et al.  Shift-invariant, multi-category phoneme recognition using Kohonen's LVQ2 , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[2]  Venta,et al.  Variants of self-organizing maps , 1989 .

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

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

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

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

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

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

[9]  E. McDermott,et al.  A hybrid speech recognition system using HMMs with an LVQ-trained codebook , 1990 .

[10]  T. Kohonen,et al.  Statistical pattern recognition with neural networks: benchmarking studies , 1988, IEEE 1988 International Conference on Neural Networks.

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