Abstract
Abstract Noise is a problem in any real-world domain. A system's sustained good performance in such domains hinges on its ability to handle noise gracefully, Explanation-based learning (EBL) systems typically ignore noise because of the insistence on generating deductive proofs. Two algorithms to handle noise in an EBL framework, using an abductive approach, are discussed. The first algorithm, using a noise-free domain theory and noise-free training examples, attempts to correct noisy test examples by matching them against the existing, operational concept. The second algorithm, based on an inductive-statistical approach, tries to recover from the effect of noisy training examples by aggregating evidence from the test examples. These algorithms have been validated using examples from two markedly different domains.
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