Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu,1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese methods revise an imperfect knowledge base (usually obtained from a domain expert) to fit a set of empirical data. Some of these methods have been successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone. Inthis paper, we describe the RAPTURE system (Revising Approximate 107 108 Mahoney and Mooney Probabilistic Theories Using Repositories of Examples), which combines connectionist andsymbolic methods to revise both the parameters and structure of a certainty-factor rule base. 2 The Rapture Algorithm