Extraction and use of contextual attributes for theory completion: an integration of explanation-based and similarity-based learning
Andrea Danyluk · 1992
Extraction and Use of Contextual Attributes for Theory Completion: An Integration of Explanation-Based and Similarity-Based Learning Andrea Pohoreckyj Danyluk This research investigates the use of contextual cues to address problems in machine learning that arise from assumptions about the initial knowledge that is necessary for the acquisition of new information. Machine learning approaches may be placed along a spectrum describing purely inductive to purely deductive techniques. Inductive systems possess essentially no explicit knowledge that can be used in acquiring new facts, while deductive systems are assumed to contain a complete theory of the domain. Most work in machine learning has concentrated on approaches at the two ends of the spectrum. This dissertation describes an approach that integrates inductive and deductive methods. It provides a mechanism by which induction can be used in order to detect and acquire knowledge missing from the domain theory of a deductive sys...