Indirect sensing through abstractive learning.

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

Download (1MB) | Preview


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: Originality: this explores the link between ideas about abstraction and ideas about indirect sensing and is also part of my general project to formulate a sensory informatics. Rigour: the paper uses informal reasoning and some informational calculations. Signification: this is interdisciplinary work relating information theory to cognitive science. Outlet: this is a hard-copy journal (or was at the time) whose status I am not quite sure about. 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: 24 Sep 2019 15:30
Google Scholar:9 Citations

View download statistics for this item

📧 Request an update