Learning and Classifying

  • Sanjay Jain
  • Eric Martin
  • Frank Stephan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6925)


We define and study a learning paradigm that sits between identification in the limit and classification. More precisely, we expect that a learner be able to identify in the limit which members of a set D of n possible data belong to a target language, where n and D are arbitrary. We show that Ex- and BC-learning are often more difficult than performing this classification task, taking into account desirable constraints on how the learner behaves, such as bounding the number of mind changes and being conservative. Special attention is given to various forms of consistency. We provide a fairly comprehensive set of results that demonstrate the fruitfulness of the approach and the richness of the paradigm.


Initial Segment Unique Member Recursive Function Inductive Inference Boolean Combination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sanjay Jain
    • 1
  • Eric Martin
    • 2
  • Frank Stephan
    • 3
  1. 1.Department of Computer ScienceNational University of SingaporeSingaporeRepublic of Singapore
  2. 2.School of Computer Science and EngineeringThe University of New South WalesSydneyAustralia
  3. 3.Department of Mathematics and Department of Computer ScienceNational University of SingaporeSingaporeRepublic of Singapore

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