A novel two phases training algorithm for a multilayer perceptron with regularization is proposed to solve a local minima problem for training networks and to enhance the generalization property of networks trained. The first phase is a trust region-based local search for fast training of networks. The second phase is an regularized line search tunneling for escaping local minima and moving toward a weight vector of next descent. These two phases are repeated alternatively in the weight space to achieve a goal training error. Benchmark results demonstrate a significant performance improvement of the proposed algorithm compared to other existing training algorithms. © Springer-Verlag 2004.
CITATION STYLE
Lee, D. W., Choi, H. J., & Lee, J. (2004). A regularized line search tunneling for efficient neural network learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3173, 239–243. https://doi.org/10.1007/978-3-540-28647-9_41
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