NEGATIVE DATA IN LEARNING LANGUAGES
Sanjay K. Jain, Efim B. Kinber · 2006
The paper is a survey of recent results on algorithmic learning (inductive inference) of languages from full collection of positive examples and some negative data. Different types of negative data are considered. We primarily concentrate on learning using (1) carefully chosen finite negative data (2) negative counterexamples provided when conjectures contain data not in the target language (3) negative counterexamples obtained from a teacher (formally, oracle), when a learner queries the oracle if an hypothesis is contained in the target language. We also explore how least counterexamples and counterexamples of bounded size fair against arbitrary counterexamples. The effects of random negative data are also briefly considered.