Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 허선 | - |
dc.date.accessioned | 2019-04-02T07:27:13Z | - |
dc.date.available | 2019-04-02T07:27:13Z | - |
dc.date.issued | 2015-08 | - |
dc.identifier.citation | 대한산업공학회지, v. 41, No. 4, Page. 338-343 | en_US |
dc.identifier.issn | 1225-0988 | - |
dc.identifier.uri | http://www.dbpia.co.kr/Journal/ArticleDetail/NODE06395928 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/101376 | - |
dc.description.abstract | We suggest a method to configure feature functions of continuous conditional random field (C-CRF). Regression tree and similarity analysis are introduced to construct the first and second feature functions of C-CRF, respectively. Rules from the regression tree are transformed to logic functions. If a logic in the set of rules is true for a data then it returns the corresponding value of leaf node and zero, otherwise. We build an Euclidean similarity matrix to define neighborhood, which constitute the second feature function. Using two feature functions, we make a C-CRF model and an illustrate example is provided. | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 대한산업공학회 | en_US |
dc.subject | Regression tree | en_US |
dc.subject | Continuous Conditional Random Field(C-CRF) | en_US |
dc.subject | Feature function | en_US |
dc.subject | Similarity | en_US |
dc.title | 회귀나무 분석을 이용한 C-CRF의 특징함수 구성 방법 | en_US |
dc.title.alternative | Method to Construct Feature Functions of C-CRF Using Regression Tree Analysis | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 41 | - |
dc.relation.page | 338-343 | - |
dc.relation.journal | 대한산업공학회지 | - |
dc.contributor.googleauthor | 안길승 | - |
dc.contributor.googleauthor | 허선 | - |
dc.relation.code | 2015040784 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING | - |
dc.identifier.pid | hursun | - |
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