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Abstract:
We present a computationally efficient algorithm for function
approximation with piecewise linear sigmoidal nodes. A one
hidden-layer network is constructed one node at a time using the
method of fitting the residual. The task of fitting individual
nodes is accomplished using a new algorithm that searches for the
best fit by solving a sequence of Quadratic Programming problems.
This approach offers significant advantages over derivative--based
search algorithms (e.g. backpropagation and its extensions). Unique
characteristics of this algorithm include: finite step convergence,
a simple stopping criterion, a deterministic methodology for
seeking "good" local minima, good scaling properties and a robust
numerical implementation.
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