Geometry of learning: Visualizing the performance of neural network supervised training methods

Author:

Androulakis G.S.,Magoulas G.D.,Vrahatis M.N.

Publisher

Elsevier BV

Subject

Applied Mathematics,Analysis

Reference12 articles.

1. Learning internal representations by error propagation;Rumelhart,1986

2. Effective backpropagation training with variable stepsize;Magoulas;Neural Networks,1996

3. Back-propagation learning for multilayer feed forward neural networks using the conjugate gradient method;Johansson;Int. J. of Neural Systems,1991

4. Minimisation methods for training feedforward neural networks;Van Der Smagt;Neural Networks,1994

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2. A technique for the visualization of population-based algorithms;2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence);2008-06

3. ADAPTIVE ALGORITHMS FOR NEURAL NETWORK SUPERVISED LEARNING: A DETERMINISTIC OPTIMIZATION APPROACH;International Journal of Bifurcation and Chaos;2006-07

4. Evolutionary algorithms for computing zeros of nonlinear functions;Nonlinear Analysis: Theory, Methods & Applications;2001-08

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