Jean-Luc Voz

Learn 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)
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)
  • 1