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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/1831
Title: Objective functions for training new hidden units in constructive neural networks
Authors: Kwok, Tin-Yau
Yeung, Dit-Yan
Keywords: Constructive algorithms
Cascade-correlation
Convergence
Input weight freezing
Quickprop
Issue Date: 1997
Citation: IEEE transactions on neural networks, v. 8, no. 5, Sept. 1997, p. 1131-1148
Abstract: In this paper, we study a number of objective functions for training new hidden units in constructive algorithms for multilayer feedforward networks. The aim is to derive a class of objective functions the computation ofwhich and the corresponding weight updates can be done in O(N) time, where N is the number of training patterns. Moreover, even though input weight freezing is applied during the process for computational efficiency, the convergence property of the constructive algorithms using these objective functions is still preserved. We also propose a few computational tricks that can be used to improve the optimization of the objective functions under practical situations. Their relative performance in a set of two-dimensional regression problems is also discussed.
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URI: http://hdl.handle.net/1783.1/1831
Appears in Collections:CSE Journal/Magazine Articles

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