Learning from interpretation transition
We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations ( I , J ) such that J = T P ( I ), where T P is the immediate consequence operator, we infer the program P . The learning framework can be repeatedly applied...
Uloženo v:
| Vydáno v: | Machine learning Ročník 94; číslo 1; s. 51 - 79 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.01.2014
Springer Nature B.V Springer Verlag |
| Témata: | |
| ISSN: | 0885-6125, 1573-0565 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (
I
,
J
) such that
J
=
T
P
(
I
), where
T
P
is the immediate consequence operator, we infer the program
P
. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata. |
|---|---|
| Bibliografie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
| ISSN: | 0885-6125 1573-0565 |
| DOI: | 10.1007/s10994-013-5353-8 |