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Title: Incremental neural network training with an increasing input dimension
Authors: Guan, SU
Liu, J
Keywords: Incremental training;Input attributes;Input dimension;Neural networks;Supervised learning;Feedforward network
Issue Date: 2004
Publisher: Freund & Pettman
Citation: Journal of Intelligent Systems. 13 (1) 43-69
Abstract: Conventional Neural Network (NN) training is done by introducing training patterns in the full input dimension under batch mode. In this paper, an incremental training method with an increasing input dimension (ITID) is presented. ITID works by dividing the whole input dimension into several sub dimensions each of which corresponds to an input attribute. Instead of learning input attributes altogether as an input vector in a training instance, NN learns input attributes one after another through their corresponding sub-networks and the NN structure is grown incrementally with an increasing input dimension. During training, information obtained from a new sub-network is merged together with the information obtained from the old ones to refine the current NN structure. With less internal interference among input attributes, ITID achieves higher generalization accuracy than the conventional method. The experiment results of several benchmark problems show that ITID is efficient and effective for both classification and regression problems.
ISSN: 0334-1860
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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