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Sign-based learning schemes for pattern classification
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DOI：10.1016/j.patrec.2005.03.013.png)
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Abstract
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This paper introduces a new class of sign-based training algorithms for neural networks that combine the sign-based updates of the Rprop algorithm with the composite nonlinear Jacobi method. The theoretical foundations of the class are described and a heuristic Rprop-based Jacobi algorithm is empirically investigated through simulation experiments in benchmark pattern classification problems. Numerical evidence shows that this new modification of the Rprop algorithm exhibits improved learning speed in all cases tested, and compares favorably against the Rprop and a recently proposed modification, the improved Rprop. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
supervised learning
nonlinear iterative methods
nonlinear Jacobi
subminimization
feed-forward neural networks
Rprop
pattern classification
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