Abstract
In this contribution a new method for supervised training is presented. This method is based on a recently proposed root finding procedure for the numerical solution of systems of non-linear algebraic and/or transcendental equations in IRn. This new method reduces the dimensionality of the problem in such a way that it can lead to an iterative approximate formula for the computation of n−1 connection weights. The remaining connection weight is evaluated separately using the final approximations of the others. This reduced iterative formula generates a sequence of points in IRn−1 which converges quadratically to the proper n−1 connection weights. Moreover, it requires neither a good initial guess for one connection weight nor accurate error function evaluations. The new method is applied on some test cases in order to evaluate its performance. Subject classification: AMS(MOS) 65K10, 49D10, 68T05, 68G05.
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Magoulas, G.D., Vrahatis, M.N., Grapsa, T.N., Androulakis, G.S. (1997). Neural Network Supervised Training Based on a Dimension Reducing Method. In: Ellacott, S.W., Mason, J.C., Anderson, I.J. (eds) Mathematics of Neural Networks. Operations Research/Computer Science Interfaces Series, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6099-9_41
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DOI: https://doi.org/10.1007/978-1-4615-6099-9_41
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