Exact and Approximate Methods for Computing the Hessian of a Feedforward Artificial Neural Network
نویسندگان
چکیده
We present two optimization techniques based on cubic curve fitting; one based on function values and derivatives a t two previous points, the other based on derivatives a t three previous points. The latter approach is viewed from a derivative space perspective, obviating the need to compute the vertical translation of the cubic, thus simplifying the fitting problem. We dieinonstrate the effectiveness of the second method in training neural networks on parity problems of various sizes, and compare our results to a modified Quickprop algorithm and to gradient descent.
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