Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1093
Title: Incremental learning with respect to new incoming input attributes
Authors: Guan, SU
Li, SC
Keywords: Constructive learning algorithms, Incremental learning, Input attributes, Neural networks, Supervised learning
Issue Date: 2001
Publisher: Springer Netherlands
Citation: Neural Processing Letters, 14(3): 241-260, Dec 2001
Abstract: Neural networks are generally exposed to a dynamic environment where the training patterns or the input attributes (features) will likely be introduced into the current domain incrementally. This paper considers the situation where a new set of input attributes must be considered and added into the existing neural network. The conventional method is to discard the existing network and redesign one from scratch. This approach wastes the old knowledge and the previous effort. In order to reduce computational time, improve generalization accuracy, and enhance intelligence of the learned models, we present ILIA algorithms (namely ILIA1, ILIA2, ILIA3, ILIA4 and ILIA5) capable of Incremental Learning in terms of Input Attributes. Using the ILIA algorithms, when new input attributes are introduced into the original problem, the existing neural network can be retained and a new sub-network is constructed and trained incrementally. The new sub-network and the old one are merged later to form a new network for the changed problem. In addition, ILIA algorithms have the ability to decide whether the new incoming input attributes are relevant to the output and consistent with the existing input attributes or not and suggest to accept or reject them. Experimental results show that the ILIA algorithms are efficient and effective both for the classification and regression problems.
URI: http://bura.brunel.ac.uk/handle/2438/1093
DOI: http://dx.doi.org/10.1023/A:1012799113953
ISSN: 1370-4621
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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