Thornton, Chris (2003) Indirect sensing through abstractive learning. Intelligent Data Analysis, 7 (3). pp. 255-266. ISSN 1088-467X

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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
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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