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Optimization for training neural nets

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1 Author(s)
Barnard, E. ; Dept. of Electron. & Comput. Eng., Pretoria Univ., South Africa

Various techniques of optimizing criterion functions to train neural-net classifiers are investigated. These techniques include three standard deterministic techniques (variable metric, conjugate gradient, and steepest descent), and a new stochastic technique. It is found that the stochastic technique is preferable on problems with large training sets and that the convergence rates of the variable metric and conjugate gradient techniques are similar

Published in:

Neural Networks, IEEE Transactions on  (Volume:3 ,  Issue: 2 )