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Thornton, Chris (2003) Indirect sensing through abstractive learning. Intelligent Data Analysis, 7 (3). pp. 255-266. ISSN 1088-467X
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Official URL: http://iospress.metapress.com/openurl.asp?genre=ar...
Abstract
The paper discusses disparity issues in sensing tasks involving the production of a 'high-level' signal from 'low-level' signal sources. It introduces an abstraction theory which helps to explain the nature of the problem and point the way to a solution. It proposes a solution based on the use of supervised adaptive methods drawn from artificial intelligence. Finally, it describes a set of empirical experiments which were carried out to evaluate the efficacy of the method.
Item Type: | Article |
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Additional Information: | Publisher's version available at official url |
Schools and Departments: | School of Engineering and Informatics > Informatics |
Subjects: | Q Science > QA Mathematics > QA0075 Electronic computers. Computer science |
Depositing User: | Chris Keene |
Date Deposited: | 29 Feb 2008 |
Last Modified: | 09 Mar 2017 06:26 |
URI: | http://srodev.sussex.ac.uk/id/eprint/1463 |
Google Scholar: | 9 Citations |
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