Sciweavers

MCS
2002
Springer

Forward and Backward Selection in Regression Hybrid Network

13 years 9 months ago
Forward and Backward Selection in Regression Hybrid Network
Abstract. We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm determines whether a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and prunes unnecessary weights. This results in a final architecture which is often much smaller than a RBF network or a MLP. Results for various data sizes on the Pumadyn data indicate that the resulting architecture competes and often outperform best known results for this data set.
Shimon Cohen, Nathan Intrator
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where MCS
Authors Shimon Cohen, Nathan Intrator
Comments (0)