Deterministic and Stochastic Logarithmic Barrier Function Methods for Neural Network Training

Author:

Trafalis Theodore B.,Tutunji Tarek A.

Publisher

Springer US

Reference52 articles.

1. Achenie, L.E.K. (1993), “A Quasi-Newton Based Approach to the Training of the Feedforward Neural Network”, Intelligent Engineering Systems through Artificial Neural Networks, Vol. 3, Editors: C. H. Dagli, L. I. Burke, B. R. Fernandez and J. Ghosh, 155–160.

2. Barnard, E. (1992), “Optimization for Training Neural Nets”, IEEE Transactions on Neural Networks, 3: 2, 232–240.

3. Battiti, R. (1992), “First-and-Second-Order Methods for Learning: Between Steepest Descent and Newton’s Method”, Neural Computation,4, 141–166.

4. Bazaraa, M.S., Sherali, H.D., and Shetty, C M., (1993/ Nonlinear Programming Theory and Algorithms, Wiley, NY.

5. Breitfeld, M. and Shanno, D. (1994), “Preliminary Computational Experience with Modified Log-Barrier Functions for Large-Scale Nonlinear programming”, Large Scale Optimization State of the Art. Hager, Hearn, and Pardalos, Editors. Kluwer Academic Publishers, 45–67.

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