Title :
Nonlinear classification by backprojection
Author_Institution :
Coll. of Eng., California Univ., Riverside, CA, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A new method to construct a classification network, called the backprojection network, by learning from a given set of training exemplars is proposed. The method is derived from an analogy with the idea of image reconstruction by backprojection in computer-aided tomography. The backprojection network is able to correctly classify any distribution of training exemplars; can be incrementally constructed; has simple weights and low connectivity; and gives predictable generalization
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); neural nets; pattern classification; backprojection network; classification network; computer-aided tomography; generalization; image reconstruction; low connectivity; nonlinear classification; Artificial neural networks; Backpropagation algorithms; Educational institutions; Image reconstruction; Multi-layer neural network; Neural networks; Pattern classification; Shape; Tomography;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374730
