An introduction to neural computing

By Teuvo Kohonen. 1988

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Citations in the corpus

Bayes risk weighted vector quantization with posterior estimation for image compression and classification (1996, methodology): “Because these design techniques are often design components of vector quantizers, and because vector quantization (VQ) has been applied successfully to both compression [2], [3] and low-level classi cation [4], [5], [6], [7], [8], [9], [10], VQ is a natural technique to explore for the combined comp”
Structure-Adaptable Digital Neural Networks (1999, methodology): “My profound acknowledgments to the experts of this thesis, Prof. I. Aleksander, Prof. J. Cabestany, Prof. W. Gerstner, Dr. P. Marchal, and Prof. E. Sanchez, and Prof. J.-D. Nicoud, president of the jury, for their careful reading and useful suggestions.”
Tourism Forecasting and its Relationship with Leading Economic Indicators (2001, methodology): “…the future, more research should be focused to apply more sophisticated forecasting techniques using the latest technology, such as neural network (Aleksander & Morton, 1990; Beale & Jackson, 1990), probabilistic rules (Wüthrich, 1996), or some other data-mining techniques to predict visitor…”
Computational Colour Science Using MATLAB (2004, background): “There are many different types of ANNs and the reader is recommended to study the extensive literature that is now available to explain the principles and algorithms of neural computing (e.g. Rumelhart and McClelland, 1986; Kohonen, 1988; Aleksander and Morton, 1991; Haykin, 1994).”