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Training Feedforward Neural Networks with the Dogleg Method and BFGS Hessian Updates
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857827IEEE-INNS-ENNS International Joint Co ...
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S.J. Perantonis, National Center for Scientific Research\DEMOKRITOS
N. Ampazis, National Center for Scientific Research\DEMOKRITOS
S. Spirou, King's College London
In this paper, we introduce an advanced optimization algorithm for training feedforward neural networks. The algorithm combines the BFGS Hessian update formula with a special case of trust region techniques, called the Dogleg method, as an altenative technique to line search methods. Simulations regarding classification and function approximation problems are presented which reveal a clear improvement both in convergence and success rates over standard BFGS implementations.
Citation:
S.J. Perantonis, N. Ampazis, S. Spirou, "Training Feedforward Neural Networks with the Dogleg Method and BFGS Hessian Updates," ijcnn, vol. 1, pp.1138, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1, 2000
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