A Functional Perspective on Machine Learning via Programmable Induction and Abduction

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...

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Bibliographic Details
Published inFunctional and Logic Programming Vol. 10818; pp. 84 - 98
Main Authors Cheung, Steven, Darvariu, Victor, Ghica, Dan R., Muroya, Koko, Rowe, Reuben N. S.
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN3319906852
9783319906850
ISSN0302-9743
1611-3349
1611-3349
DOI10.1007/978-3-319-90686-7_6

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Summary: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.
ISBN:3319906852
9783319906850
ISSN:0302-9743
1611-3349
1611-3349
DOI:10.1007/978-3-319-90686-7_6