Combining Numeric and Symbolic Learning Techniques
Incremental learning from examples in a noisy domain is a difficult problem in machine learning. In this paper we divide the task into two subproblems and present a combination of numeric and symbolic approaches that yields robust learning of boolean characterizations. We have implemented this method in a computer program and present its empirical learning performance in the presence of varying amounts of noise.
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