Very Fast Online Learning of Highly Non Linear Problems
Aggelos Chariatis; 8(69):2017−2045, 2007.
The experimental investigation on the efficient learning of highly non-linear problems by online training, using ordinary feed forward neural networks and stochastic gradient descent on the errors computed by back-propagation, gives evidence that the most crucial factors for efficient training are the hidden units' differentiation, the attenuation of the hidden units' interference and the selective attention on the parts of the problems where the approximation error remains high. In this report, we present global and local selective attention techniques and a new hybrid activation function that enables the hidden units to acquire individual receptive fields which may be global or local depending on the problem's local complexities. The presented techniques enable very efficient training on complex classification problems with embedded subproblems.
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