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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Browne, A. Hudson, B. Whitley, D. Ford, M. Picton, P. Kazemian, H. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. of Comput., Surrey Univ., Guildford, UK (Browne, A.) |
| Abstract | In the past, neural networks have been viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed which extract comprehensible representations from trained neural networks, enabling them to be used for data mining, i.e. the discovery and explanation of previously unknown relationships present in data. This paper reviews existing algorithms for extracting comprehensible representations from neural networks and describes research to generalize and extend the capabilities of one of these algorithms. The algorithm has been generalized for application to bioinformatics datasets, including the prediction of splice site junctions in human DNA sequences. Results generated on this dataset are compared with those generated by a conventional data mining technique (C5), and conclusions are drawn regarding the application of the neural network based technique to other fields of interest. |
| Sponsorship | IEEE Industrial Electronics Soc |
| Starting Page | 1909 |
| Ending Page | 1913 |
| File Size | 433512 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780379063 |
| DOI | 10.1109/IECON.2003.1280352 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-11-02 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Data mining Decision trees Councils Algorithm design and analysis Bioinformatics Humans DNA Sequences Learning |
| Content Type | Text |
| Resource Type | Article |
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