Indirect sensing through abstractive learning
Chris Thornton · Intelligent Data Analysis · 2003
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 s upervised 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.