Name
Title | ||
|---|---|---|
Generalizations in typed equational programming and their application to learning functions |
Abstract | ||
|---|---|---|
In this paper we investigate generalization methods in typed equational programming and apply them to inductive inference
of functions. We are interested in inducing programs from given examples which are input-output pairs. Our main contribution
is a new generalization algorithm which uses type polymorphism. With the algorithm we introduce, for the first time, a generalization
phase to Summers’ method. Moreover, we present a new bottom-up inference method which combines elements of the generalization
algorithm, a minimal multiple generalization algorithm, and Summers’ method. This integration is enabled with the adaptation
of equational programming. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1007/BF03037561 | New Generation Comput. |
Keywords | Field | DocType |
equational programming,polymorphism,input output,machine learning,inductive inference,generalization,generic algorithm,bottom up | Inductive reasoning,Programming language,Inference,Computer science,Generalization,Theoretical computer science,Software,Artificial intelligence,Machine learning,Equational programming | Journal |
Volume | Issue | ISSN |
15 | 1 | 0288-3635 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Ishino | 1 | 54 | 7.31 |
| Akihiro Yamamoto | 2 | 135 | 26.84 |