Relational Evidence Theory and Interpreting Schematics

Abstract

A new relational learning algorithm, the Consolidated Learning Algorithm based on Relational Evidence Theory (CLARET) is presented. Here, two different approaches to evidential learning are consolidated in how they apply to generalising within relational data structures. Attribute-based discrimination (decision trees) is integrated with part-based interpretation (graph matching) for evaluating and updating representations in spatial domains. This allows an interpretation stage to be incorporated into the generalisation process. These components of the system are demonstrated in an on-line system for the recognition of hand drawn, schematic diagrams and spatial symbols. The approach uses an a..