Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/93357
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dc.contributorSchool of Accounting and Financeen_US
dc.creatorYang, YCen_US
dc.creatorKe, YSen_US
dc.creatorWu, Wen_US
dc.creatorLin, KPen_US
dc.creatorJin, Yen_US
dc.date.accessioned2022-06-21T08:22:07Z-
dc.date.available2022-06-21T08:22:07Z-
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10397/93357-
dc.description6th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019, Orlando, FL, USA, July 26-31, 2019en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© Springer Nature Switzerland AG 2019.en_US
dc.rightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-22338-0_12en_US
dc.subjectFinancial kernelen_US
dc.subjectMachine learningen_US
dc.subjectMergers and acquisitionsen_US
dc.subjectRecommendation serviceen_US
dc.subjectSupport vector machineen_US
dc.titleRecommendation as a service in mergers and acquisitions transactionsen_US
dc.typeConference Paperen_US
dc.identifier.spage151en_US
dc.identifier.epage159en_US
dc.identifier.volume11589en_US
dc.identifier.doi10.1007/978-3-030-22338-0_12en_US
dcterms.abstractMergers and acquisitions (M&A) happens frequently between corporations to combine and/or transfer their ownerships, operating units and assets. The purpose of the study is to develop a service that is able to recommend a feasible M&A deal. We integrate the support vector machine model with the kernel tricks to automatically determine M&A deals. In the end of the study, our proposed technique is empirically validated, and the results show the effectiveness of the recommendation service.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2019, v. 11589, p. 151-159en_US
dcterms.isPartOfLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)en_US
dcterms.issued2019-
dc.identifier.scopus2-s2.0-85069836447-
dc.relation.conferenceInternational Conference on HCI in Business, Government, and Organizations [HCIBGO]en_US
dc.identifier.eissn1611-3349en_US
dc.description.validate202206 bcfcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAF-0113-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work is supported by the Ministry of Science and Technology of Taiwan (106-2410-H-110-082), and the Intelligent Electronic Commerce Research Center from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS25851965-
Appears in Collections:Conference Paper
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