Algorithmic Learning Theory

Volume 4754 of the series Lecture Notes in Computer Science pp 257-271

One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples

  • Sanjay JainAffiliated withSchool of Computing, National University of Singapore, 117590
  • , Efim KinberAffiliated withDepartment of Computer Science, Sacred Heart University, Fairfield, CT 06432-1000

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As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form of corrected or rephrased sentences. In this paper, we suggest one approach to formalization of overt and covert evidence within the framework of one-shot learners via subset and membership queries to a teacher (oracle). We compare and explore general capabilities of our models, as well as complexity advantages of learnability models of one type over models of other types, where complexity is measured in terms of number of queries. In particular, we establish that “correcting” positive examples give sometimes more power to a learner than just negative (counter)examples and access to full positive data.