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dc.contributor.authorHolmes, Geoffreyen_NZ
dc.contributor.authorLiu, Tie-Yanen_NZ
dc.contributor.authorLi, Hangen_NZ
dc.contributor.authorKing, Irwinen_NZ
dc.contributor.authorSugiyama, Masashien_NZ
dc.contributor.authorZhou, Zhi-Huaen_NZ
dc.contributor.editorHolmes, Geoffreyen_NZ
dc.contributor.editorLiu, Tie-Yanen_NZ
dc.contributor.editorKing, Irwinen_NZ
dc.contributor.editorSugiyama, Masashien_NZ
dc.contributor.editorZhou, Zhi-Huaen_NZ
dc.date.accessioned2017-05-17T21:44:43Z
dc.date.available2017en_NZ
dc.date.available2017-05-17T21:44:43Z
dc.date.issued2017en_NZ
dc.identifier.citationHolmes, G., Liu, T.-Y., Li, H., King, I., Sugiyama, M., & Zhou, Z.-H. (2017). Introduction: Special Issue of Selected Papers from ACML 2015. Machine Learning, 106(4), 459–461. https://doi.org/10.1007/s10994-017-5636-6en
dc.identifier.issn0885-6125en_NZ
dc.identifier.urihttp://hdl.handle.net/10289/11057
dc.description.abstractWe are delighted to present this special issue of Machine Learning Journal with selected papers from the Seventh Asian Conference on Machine Learning (ACML 2015) held in Hong Kong, from 20 to 22 November 2015. ACML aims at providing a leading international forum for researchers in machine learning and related fields to share their new ideas and achievements. While located in Asia, the conference has a wide visibility to the international community. ACML was the first machine learning conference with two cycles of submissions with a strict double-blind review process, and this tradition continues.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights© The Author(s) 2017
dc.subjectcomputer scienceen_NZ
dc.subjectmachine learningen_NZ
dc.titleIntroduction: Special Issue of Selected Papers from ACML 2015en_NZ
dc.typeJournal Article
dc.identifier.doi10.1007/s10994-017-5636-6en_NZ
dc.relation.isPartOfMachine Learningen_NZ
pubs.begin-page459
pubs.elements-id193862
pubs.end-page461
pubs.issue4en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/2018 PBRF
pubs.organisational-group/Waikato/FCMS
pubs.organisational-group/Waikato/FCMS/2018 PBRF - FCMS
pubs.publication-statusPublisheden_NZ
pubs.volume106en_NZ
dc.identifier.eissn1573-0565en_NZ


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