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dc.contributor.authorOu, Ge
dc.contributor.authorWang, Yan
dc.contributor.authorHuang, Lan
dc.contributor.authorPang, Wei
dc.contributor.authorCoghill, George MacLeod
dc.contributor.editorPhung, Dinh
dc.contributor.editorTseng, Vincent S.
dc.contributor.editorWebb, Prof. Geoffrey I.
dc.contributor.editorHo, Bao
dc.contributor.editorGanji, Mohadeseh
dc.contributor.editorRashidi, Lida
dc.date.accessioned2019-07-21T23:01:45Z
dc.date.available2019-07-21T23:01:45Z
dc.date.issued2018-07-22
dc.identifier.citationOu , G , Wang , Y , Huang , L , Pang , W & Coghill , G M 2018 , ε-Distance Weighted Support Vector Regression . in D Phung , V S Tseng , P G I Webb , B Ho , M Ganji & L Rashidi (eds) , Advances in Knowledge Discovery and Data Mining : 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I . , 17 , Lecture Notes in Artificial Intelligence , Springer International Publishing , PAKDD 2018 , Melbourne , Australia , 3/06/18 . https://doi.org/10.1007/978-3-319-93034-3_17en
dc.identifier.citationconferenceen
dc.identifier.isbn9783319930336
dc.identifier.isbn9783319930343
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 116862925
dc.identifier.otherPURE UUID: 6f5f7d9e-5963-4af6-a974-8b30050e30aa
dc.identifier.otherScopus: 85049381165
dc.identifier.otherORCID: /0000-0002-1761-6659/work/52133917
dc.identifier.otherORCID: /0000-0002-1761-6659/work/59923270
dc.identifier.otherORCID: /0000-0002-2047-8277/work/63561533
dc.identifier.urihttp://hdl.handle.net/2164/12610
dc.descriptionWe gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the source code of “LDM”, and their kind technical assistance. We also thank Prof Chih-Jen Lins team for providing the LIBSVM and LIBLINEAR packages and their support. This work is supported by the National Natural Science Foundation of China (Grant Nos.61472159, 61572227) and Development Project of Jilin Province of China (Grant Nos. 20140101180JC, 20160204022GX, 20180414012G H). This work is also partially supported by the 2015 Scottish Crucible Award funded by the Royal Society of Edinburgh and the 2016 PECE bursary provided by the Scottish Informatics & Computer Science Alliance (SICSA).en
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofAdvances in Knowledge Discovery and Data Miningen
dc.relation.ispartofseriesLecture Notes in Artificial Intelligenceen
dc.rightsThe final authenticated version is available online at https://doi.org/10.1007/978-3-319-93034-3_17en
dc.subjectregression analysisen
dc.subjectSupport Vector Regressionen
dc.subjectDistance Weighted Support Vector Regressionen
dc.subjectDual Coordinate Descenten
dc.subjectAveraged Stochastic Gradient Descenten
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleε-Distance Weighted Support Vector Regressionen
dc.typeConference itemen
dc.contributor.institutionUniversity of Aberdeen.Institute for Complex Systems and Mathematical Biology (ICSMB)en
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Computer Science and Informaticsen
dc.description.versionPostprinten
dc.identifier.doihttps://doi.org/10.1007/978-3-319-93034-3_17
dc.date.embargoedUntil2019-07-22


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