Difference between revisions of "Publications:ML-like Inference for Classifiers"
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Latest revision as of 04:44, 26 June 2014
| Title | ML-like Inference for Classifiers |
|---|---|
| Author | Cristiano Calcagno and Eugenio Moggi and Walid Taha |
| Year | 2004 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | |
| DOI | |
| Conference | ESOP'04. European Symposium on Programming |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:588275 |
| Abstract | Environment classifiers were proposed as a new approach totyping multi-stage languages. Safety was established in the simply-typedand let-polymorphic settings. While the motivation for classifiers was thefeasibility of inference, this was in fact not established. This paper startswith the observation that inference for the full classifier-based systemfails. We then identify a subset of the original system for which inferenceis possible. This subset, which uses implicit classifiers, retains significantexpressivity (e.g. it can embed the calculi of Davies and Pfenning) andeliminates the need for classifier names in terms. Implicit classifiers wereimplemented in MetaOCaml, and no changes were needed to make anexisting test suite acceptable by the new type checker. |
