Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/56315
Title: Incremental ordered neural network training
Authors: Guan, S.-U. 
Liu, J.
Keywords: Incremental training
Input attributes
Neural networks
Ordered training
Issue Date: 2002
Source: Guan, S.-U.,Liu, J. (2002). Incremental ordered neural network training. Journal of Intelligent Systems 12 (3) : 137-172. ScholarBank@NUS Repository.
Abstract: This paper investigates the incremental training of a Neural Network (NN) with the input attributes introduced in order. A specially designed NN is used to evaluate the individual discrimination ability of each input attribute. Attributes are then sorted in descending, ascending, and random orders of their individual discrimination abilities and introduced into another NN being trained with an incremental training algorithm, ITID. To reduce the interference caused by irrelevant features and high-complexity tasks, only relevant features are involved and tasks are decomposed in the experiments. The experimental results of several benchmark problems show that descending order obtains the highest generalization accuracy among the three training orders for both classification and regression problems.
Source Title: Journal of Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/56315
ISSN: 03341860
Appears in Collections:Staff Publications

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