Input-Dependence in Function-Learning

  • Sanjay Jain
  • Eric Martin
  • Frank Stephan
Conference paper

DOI: 10.1007/978-3-540-73001-9_39

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4497)
Cite this paper as:
Jain S., Martin E., Stephan F. (2007) Input-Dependence in Function-Learning. In: Cooper S.B., Löwe B., Sorbi A. (eds) Computation and Logic in the Real World. CiE 2007. Lecture Notes in Computer Science, vol 4497. Springer, Berlin, Heidelberg

Abstract

In the standard literature on inductive inference, a learner sees as input the course of values of the function to be learned. In the present work, it is investigated how reasonable this choice is and how sensitive the model is with respect to variations like the overgraph or undergraph of the function. Several implications and separations are shown and for the basic notions, a complete picture is obtained. Furthermore, relations to oracles, additional information and teams are explored.

Keywords

Inductive inference recursion theory various forms of input presentation team learning learning with additional information 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sanjay Jain
    • 1
  • Eric Martin
    • 2
  • Frank Stephan
    • 3
  1. 1.Department of Computer Science, National University of Singapore, Singapore 117543Republic of Singapore
  2. 2.Department of Computer Science and Engineering, The University of New South Wales, Sydney 2052, NSWCommonwealth of Australia
  3. 3.Department of Mathematics, National University of Singapore, Singapore 117543Republic of Singapore

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