sweeping hinge algorithm
Function Approximation with the Sweeping Hinge Algorithm
We present a computationally efficient algorithm for function ap(cid:173) proximation 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 accom(cid:173) plished using a new algorithm that searchs for the best fit by solving a sequence of Quadratic Programming problems. Unique characteristics of this algorithm include: finite step convergence, a simple stop(cid:173) ping criterion, a deterministic methodology for seeking "good" local minima, good scaling properties and a robust numerical implemen(cid:173) tation.
Function Approximation with the Sweeping Hinge Algorithm
Hush, Don R., Lozano, Fernando, Horne, Bill G.
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 searchs for the best fit by solving a sequence of Quadratic Programming problems. This approach offers significant advantages over derivative-based search algorithms (e.g.
Function Approximation with the Sweeping Hinge Algorithm
Hush, Don R., Lozano, Fernando, Horne, Bill G.
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 searchs for the best fit by solving a sequence of Quadratic Programming problems. This approach offers significant advantages over derivative-based search algorithms (e.g.
Function Approximation with the Sweeping Hinge Algorithm
Hush, Don R., Lozano, Fernando, Horne, Bill G.
We present a computationally efficient algorithm for function approximation withpiecewise 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 usinga new algorithm that searchs for the best fit by solving a sequence of Quadratic Programming problems. This approach offers significantadvantages over derivative-based search algorithms (e.g.