A Functional Perspective on Machine Learning via Programmable Induction and Abduction. / Cheung, Steven; Davariu, Victor; Ghica, Dan; Muroya, Koko; Rowe, Reuben.
FLOPS 2018: Functional and Logic Programming. Vol. 10818 Springer, 2018. p. 84-98 (Lecture Notes in Computer Science).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
E-pub ahead of print
Documents
- Accepted Manuscript
Accepted author manuscript, 332 KB, PDF document
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.
| Original language | English |
|---|---|
| Title of host publication | FLOPS 2018 |
| Subtitle of host publication | Functional and Logic Programming |
| Publisher | Springer |
| Pages | 84-98 |
| Number of pages | 15 |
| Volume | 10818 |
| ISBN (Electronic) | 978-3-319-90686-7 |
| ISBN (Print) | 978-3-319-90685-0 |
| DOIs | |
| Publication status | E-pub ahead of print - 24 Apr 2018 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
ID: 34708358