DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, H | - |
dc.date.accessioned | 2016-04-01T02:21:14Z | - |
dc.date.available | 2016-04-01T02:21:14Z | - |
dc.date.created | 2011-03-28 | - |
dc.date.issued | 2011-01 | - |
dc.identifier.issn | 1384-5810 | - |
dc.identifier.other | 2011-OAK-0000023112 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24968 | - |
dc.description.abstract | As many databases have been brought online, data retrieval-finding relevant data from large databases-has become a nontrivial task. A feedback-based data retrieval system was proposed to provide user with an intuitive way for expressing their preferences in queries. The system iteratively receives a partial ordering on a sample of data from the user, learns a ranking function, and returns highly ranked results according to the function. An important issue in such retrieval systems is minimizing the number of iterations or the amount of feedback to learn an accurate ranking function. This paper proposes selective sampling (or active learning) techniques for RankSVM that can be used in the retrieval systems. The proposed techniques minimizes the amount of user interaction to learn an accurate ranking function thus facilitates users formulating a preference query in the data retrieval system. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | DATA MINING AND KNOWLEDGE DISCOVERY | - |
dc.subject | Selective sampling | - |
dc.subject | Feedback-based data retrieval | - |
dc.title | Selective sampling techniques for feedback-based data retrieval | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1007/S10618-010-0168-8 | - |
dc.author.google | Yu, H | - |
dc.relation.volume | 22 | - |
dc.relation.issue | 1 | - |
dc.relation.startpage | 1 | - |
dc.relation.lastpage | 30 | - |
dc.contributor.id | 10162777 | - |
dc.relation.journal | DATA MINING AND KNOWLEDGE DISCOVERY | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | DATA MINING AND KNOWLEDGE DISCOVERY, v.22, no.1, pp.1 - 30 | - |
dc.identifier.wosid | 000286001100001 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.endPage | 30 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | DATA MINING AND KNOWLEDGE DISCOVERY | - |
dc.citation.volume | 22 | - |
dc.contributor.affiliatedAuthor | Yu, H | - |
dc.identifier.scopusid | 2-s2.0-78651366861 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 6 | - |
dc.description.scptc | 8 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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