Abstract
Two different approaches in constructing Neural Network (NN) classifiers are discussed - discriminant-based networks and Region of Influence networks. A general model for ROI networks is presented, and the different functionalities of this structure are discussed: classification, vector quantization and associative memory. Also, an architecture for this model’s implementation is presented, and the hardware realization of each layer is reviewed in detail.
Cite
CITATION STYLE
Castillo, F., Cabestany, J., & Moreno, J. M. (1993). Region of influence (ROI) networks. Model and implementation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 686, pp. 96–101). Springer Verlag. https://doi.org/10.1007/3-540-56798-4_130
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