Philippe Thissen

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This paper presents a compression scheme for digital still images, by using the Kohonen's neural network algorithm, not only for its vector quantization feature, but also for its topological property. This property allows an increase of about 80% for the compression rate. Compared to the JPEG standard, this compression scheme shows better performances (in(More)
Many neural-like algorithms cummtly under study support classification tasks. Several of these algorithms base their functionality on LVQ-like procedures to find locations of centroids in the data space, and on kernel (or radial-basis) functions centered on these centroids to approximate functions or probability densities. A generic analog chip could(More)
Various types of neural networks may b e u s e d i n m ulti-dimensional classiication taskss among them, Bayesian and LVQ algorithms are interesting respectively for their performances and their simplicity of operations. The large number of operations involved in such algorithms may h o wever be incompatible with on-line applications or with the necessity(More)
For pattern classiication in a multi-dimensional space, the minimum misclassiication rate is obtained by using the Bayes criterion. Kernel estimators or probabilistic neural networks provide a good way t o e v aluate the probability densities of each class of data and are an interesting parallel implementation of the Bayesian classiier 1]. However, their(More)
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