ML-like Inference for Classifiers
2004 (English)In: Programming Languages and Systems: 13th European Symposium on Programming, ESOP 2004, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2004, Barcelona, Spain, March 29 - April 2, 2004. Proceedings / [ed] David Schmidt, Heidelberg: Springer Berlin/Heidelberg, 2004, Vol. 2986, p. 79-93Conference paper, Published paper (Refereed)
Abstract [en]
Environment classifiers were proposed as a new approach to typing multi-stage languages. Safety was established in the simply-typed and let-polymorphic settings. While the motivation for classifiers was the feasibility of inference, this was in fact not established. This paper starts with the observation that inference for the full classifier-based system fails. We then identify a subset of the original system for which inference is possible. This subset, which uses implicit classifiers, retains significant expressivity (e.g. it can embed the calculi of Davies and Pfenning) and eliminates the need for classifier names in terms. Implicit classifiers were implemented in MetaOCaml, and no changes were needed to make an existing test suite acceptable by the new type checker. © Springer-Verlag 2004.
Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2004. Vol. 2986, p. 79-93
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 2986
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-20976DOI: 10.1007/978-3-540-24725-8_7ISI: 000189481600007Scopus ID: 2-s2.0-35048882935Libris ID: 9467151ISBN: 978-3-540-21313-0 ISBN: 978-3-540-24725-8 OAI: oai:DiVA.org:hh-20976DiVA, id: diva2:588275
Conference
13th European Symposium on Programming, ESOP 2004, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2004, Barcelona, Spain, March 29 - April 2, 2004
Note
E. Moggi supported by MIUR project NAPOLI, EU project DART IST-2001-33477 and thematic network APPSEM II IST-2001-38957. W. Taha supported by NSF ITR-0113569 and NSF CCR-0205542.
2013-01-152013-01-142021-05-11Bibliographically approved