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A Functional Perspective on Machine Learning via Programmable Induction and Abduction

  • Steven Cheung
  • Victor Darvariu
  • Dan R. Ghica
  • Koko Muroya
  • Reuben N. S. Rowe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10818)

Abstract

We present a programming language for machine learning based on the concepts of ‘induction’ and ‘abduction’ as encountered in Peirce’s logic of science. We consider the desirable features such a language must have, and we identify the ‘abductive decoupling’ of parameters as a key general enabler of these features. Both an idealised abductive calculus and its implementation as a PPX extension of OCaml are presented, along with several simple examples.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of BirminghamBirminghamUK
  2. 2.University of KentCanterburyUK
  3. 3.RIMSKyoto UniversityKyotoJapan

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