Indirect sensing through abstractive learning.

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

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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
Google Scholar:9 Citations

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