Learners Based on Transducers

  • Sanjay Jain
  • Shao Ning Kuek
  • Eric Martin
  • Frank Stephan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10792)

Abstract

As data come out one by one from an infinite stream, automatic learners maintain some string as long term memory, and update it at every new datum (example) they process. Transduced learners are generalization of automatic learners. Both kind of learners are evaluated with respect to the space they consume for learning. For automatic learners, it is unknown whether at any point, the size of the long term memory can be bounded by the length of the longest datum that has been received so far. Here it is shown that, even when restricting learning to automatic families, there is a hierarchy of classes that can be learnt with memory \(O(n^k)\), and all automatic families which are learnable in principle can be learnt by a transduced learner using exponential sized memory.

Keywords

Automata and logic Inductive inference Transducers 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

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

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