SUPERVISED TRAINING USING GLOBAL SEARCH METHODS
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George D. Magoulas | Vassilis P. Plagianakos | Michael N. Vrahatis | M. N. Vrahatis | G. D. Magoulas | V. Plagianakos
[1] Alberto Tesi,et al. On the Problem of Local Minima in Backpropagation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[2] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.
[3] Sandro Ridella,et al. Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.
[4] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[5] X H Yu,et al. On the local minima free condition of backpropagation learning , 1995, IEEE Trans. Neural Networks.
[6] Stephen T. Welstead,et al. Neural network and fuzzy logic applications in C/C++ , 1994, Wiley professional computing.
[7] Vassilis P. Plagianakos,et al. Training neural networks with threshold activation functions and constrained integer weights , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[8] Christopher R. Houck,et al. A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .
[9] George D. Magoulas,et al. Neural network supervised training based on a dimension reducing method , 1997 .
[10] Vassilis P. Plagianakos,et al. Neural network training with constrained integer weights , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[11] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.
[12] George D. Magoulas,et al. Effective Backpropagation Training with Variable Stepsize , 1997, Neural Networks.
[13] Sang-Hoon Oh,et al. An analysis of premature saturation in back propagation learning , 1993, Neural Networks.
[14] Michael N. Vrahatis,et al. On the alleviation of the problem of local minima in back-propagation , 1997 .
[15] E. K. Blum,et al. Approximation of Boolean Functions by Sigmoidal Networks: Part I: XOR and Other Two-Variable Functions , 1989, Neural Computation.
[16] Patrick van der Smagt. Minimisation methods for training feedforward neural networks , 1994, Neural Networks.
[17] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[18] Martin Fodslette Meiller. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .
[19] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[20] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[21] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[22] Robert M. Burton,et al. Event-dependent control of noise enhances learning in neural networks , 1992, Neural Networks.