The book we have at hand is the fourth monograph I wrote for Springer Verlag. The previous one named "Self-Organization and Associative Mem ory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works re viewed in Chap. 9. The new results obtained in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book. I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step. I became interested in neural networks around 1960, but could not in terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Justification of Neural Modeling
The Basic SOM
in the Output Plane
Physiological Interpretation of SOM
Variants of SOM 143
Learning Vector Quantization
Other editions - View all
accuracy Acoustics algorithm Amsterdam applications approximation array Artificial Intelligence Artificial Neural Networks cell classification clustering codebook vectors computing Congress on Neural context convergence cortex defined density function dimensionality distance dot product elements equations Euclidean IEEE Service Center IJCNN-93-Nagoya input vector Joint Conf Kangas Kohonen learning linear Mäkisara Marinaro mathematical matrix method mi(t neighborhood function Netherlands Netherlands 1990 Networks IEEE Service Networks Lawrence Erlbaum Neural Networks IEEE Neural Networks Lawrence neuron nodes nonlinear North-Holland operation optimal orthogonal output P. G. Morasso Springer parameters Pattern Recognition phonemes Piscataway probability density function problems Proc Processing IEEE Service reference vectors samples scalar Sect self-organizing Self-Organizing Map sequences Signal Processing Signal Processing IEEE Simula simulation Speech and Signal speech recognition statistical stochastic subset subspace supervised learning symbols synaptic topology two-dimensional values vector quantization Voronoi Voronoi tessellation whereby winner