An Anytime Approach To Connectionist Theory Refinement: Refining The Topologies Of Knowledge-Based Neural Networks (1995)
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@TECHREPORT{Opitz95ananytime,
author = {David William Opitz},
title = {An Anytime Approach To Connectionist Theory Refinement: Refining The Topologies Of Knowledge-Based Neural Networks},
institution = {},
year = {1995}
}
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Abstract
Many scientific and industrial problems can be better understood by learning from samples of the task at hand. For this reason, the machine learning and statistics communities devote considerable research effort on generating inductive-learning algorithms that try to learn the true "concept" of a task from a set of its examples. Often times, however, one has additional resources readily available, but largely unused, that can improve the concept that these learning algorithms generate. These resources include available computer cycles, as well as prior knowledge describing what is currently known about the domain. Effective utilization of available computer time is important since for most domains an expert is willing to wait for weeks, or even months, if a learning system can produce an improved concept. Using prior knowledge is important since it can contain information not present in the current set of training examples. In this thesis, I present three "anytime" approaches to connec...
