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A new class of quasi-newtonian methods for optimal learning in mlp-networks
2003
IEEE Transactions on Neural Networks
In this paper, we present a new class of quasi-Newton methods for the effective learning in large multilayer perceptron (MLP)-networks. The algorithms introduced in this work, named QN, utilize an iterative scheme of a generalized BFGS-type method, involving a suitable family of matrix algebras . The main advantages of these innovative methods are based upon the fact that they have an ( log ) complexity per step and that they require ( ) memory allocations. Numerical experiences, performed on a
doi:10.1109/tnn.2003.809425
pmid:18238010
fatcat:vor5ukyrtfg2dnb3on3droe3xm