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Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Zheng, Huiru and Wang, Haiying (2012) Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations. International Journal of Machine Learning and Cybernetics, 3 (3). pp. 173-182. [Journal article]

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URL: http://dx.doi.org/10.1007/s13042-011-0050-z

DOI: 10.1007/s13042-011-0050-z

Abstract

The ability to reveal the relevant patterns in an intuitively attractive way through incremental learning makes self-adaptive neural networks (SANNs) a power tool to support pattern discovery and visualisation. Based on the combination of the information related to both the shape and magnitude of the data, this paper introduces a SANN, which implements new similarity matching criteria and error accumulation strategies for network growth. It was tested on two datasets including a real biological gene expression dataset. The results obtained have demonstrated several significant features exhibited by the proposed SANN model for improving pattern discovery and visualisation.

Item Type:Journal article
Keywords:Self-adaptive neural networks – Pattern discovery and visualisation – Similarity measure – Chi-squares statistics
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:23212
Deposited By:Dr Huiru Zheng
Deposited On:31 Aug 2012 12:07
Last Modified:31 Aug 2012 12:07

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