Support logic for feature representation, pattern recognition and machine learning

Abstract

The formalism of support logic provides a framework for deductive inference, with mathematically sound and consistent treatment of uncertainty and evidence which is aggregated through the reasoning process. The authors apply support logic programming to pattern recognition. Initially, a pattern classifier is constructed by encoding expert knowledge of the problem domain into rules of support logic. Fuzzy sets allow the general properties of features to be described precisely. Semantic unification provides an alternative to the usual metric-based similarity criteria. The validity of the approach is established by cross-validating the support logic classifier against models from alternative paradigms. The authors then attempt to circumvent the requirement for a domain expert, and assess the extent to which data-driven learning processes can be used to automatically derive components of the support logic classifier.<>