A Hybrid Framework Combining Structural and Decision-Theoretic Pattern Recognition and Applications

A new framework is introduced which allows the formulation of difficult structural classification tasks in terms of decision-theoretic-based pattern recognition. It is based on extending the classical formulation of generalized linear discriminant functions so as to permit each given object to have a different vector representation in each class. The proposed extension properly accounts for the corresponding extension of the classical learning techniques of linear discriminant functions in a way such that the convergence of the extended techniques can still be proved. The proposed framework can be considered as a hybrid methodology in which both structural and decision-theoretic pattern recognition are integrated. Furthermore, it can be considered as a means to achieve convenient tradeoffs between the inductive and deductive ways of knowledge acquisition, which can result in rendering tractable the possibly hard original inductive learning problem associated with the given task. The proposed framework and methods are illustrated through their use in two difficult structural classification tasks, showing both the appropriateness and the capability of these methods to obtain useful results.