Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Learnability of probabilistic automata via oracles

Simple item page
dc.contributor.authorGuttman, Omri
dc.contributor.authorVishwanathan, S
dc.contributor.authorWilliamson, Robert
dc.coverage.spatialSingapore
dc.date.accessioned2015-12-13T22:55:02Z
dc.date.createdOctober 8-11 2005
dc.date.issued2005
dc.date.updated2015-12-11T11:07:58Z
dc.description.abstractEfficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In
dc.identifier.isbn354029242X
dc.identifier.urihttp://hdl.handle.net/1885/82367
dc.publisherSpringer
dc.relation.ispartofseriesInternational Conference on Algorithmic Learning Theory (ALT 2005)
dc.sourceAlgorithmic Learning Theory: Proceedings of the 16th International Conference on Algorithmic Learning Theory (ALT-05) - LNAI 3734
dc.source.urihttp://www.springerlink.com/content/4l3kjpj6kjw7
dc.subjectKeywords: Algorithms; Learning systems; Probabilistic logics; Theorem proving; Learnability; Oracles; State merging algorithms; Automata theory
dc.titleLearnability of probabilistic automata via oracles
dc.typeConference paper
local.bibliographicCitation.lastpage182
local.bibliographicCitation.startpage171
local.contributor.affiliationGuttman, Omri, College of Engineering and Computer Science, ANU
local.contributor.affiliationVishwanathan, S, College of Engineering and Computer Science, ANU
local.contributor.affiliationWilliamson, Robert, College of Engineering and Computer Science, ANU
local.contributor.authoruidGuttman, Omri, u2510624
local.contributor.authoruidVishwanathan, S, a204054
local.contributor.authoruidWilliamson, Robert, u9000163
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub10622
local.identifier.doi10.1007/11564089_15
local.identifier.scopusID2-s2.0-33646529351
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
01_Guttman_Learnability_of_probabilistic_2005.pdf
Size:
194.4 KB
Format:
Adobe Portable Document Format
Download

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS