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Preface [to] Theoretical Computer Science, Vol. 410 (19)

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dc.contributor.authorHutter, Marcus
dc.contributor.authorServedio, Rocco A.
dc.date.accessioned2015-08-26T05:38:33Z
dc.date.available2015-08-26T05:38:33Z
dc.date.issued2009-04-28
dc.description.abstractThis special issue contains expanded versions of papers that appeared in preliminary form in the proceedings of the 18th International Conference on Algorithmic Learning Theory (ALT 2007), which was held in Sendai, Japan, during October 1–4, 2007. Algorithmic Learning Theory is a conference series which is dedicated to the theoretical study of the algorithmic aspects of learning. The best papers of the conference ALT 2007 were invited for this special issue and after a thorough reviewing process, most of them qualified for this Special Issue on Algorithmic Learning Theory of Theoretical Computer Science. In the following, a short introduction is given to each of these papers.en_AU
dc.identifier.issn0304-3975en_AU
dc.identifier.urihttp://hdl.handle.net/1885/14966
dc.publisherElsevieren_AU
dc.rights© 2009 Elsevier B.V.en_AU
dc.sourceTheoretical Computer Scienceen_AU
dc.subjectalgorithmic learning theoryen_AU
dc.titlePreface [to] Theoretical Computer Science, Vol. 410 (19)en_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue19en_AU
local.bibliographicCitation.lastpage1748en_AU
local.bibliographicCitation.startpage1747en_AU
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.authoruidu4350841en_AU
local.description.embargo2037-12-31
local.identifier.citationvolume410en_AU
local.identifier.doi10.1016/j.tcs.2009.01.008en_AU
local.publisher.urlhttp://www.elsevier.com/en_AU
local.type.statusPublished Versionen_AU

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