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Calculating different weights in feature values in logistic regression
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, C.-H. | - |
| dc.date.accessioned | 2024-08-08T06:30:35Z | - |
| dc.date.available | 2024-08-08T06:30:35Z | - |
| dc.date.issued | 2016-11-26 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/18913 | - |
| dc.description.abstract | In traditional logistic regression model, every value of feature has the same weight. In this paper, we propose a new weighting method for logistic regression, which assigns a different weight to each feature value. A gradient approach is used to calculate the optimal weights of feature values. © 2016 ACM. | - |
| dc.format.extent | 3 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Association for Computing Machinery | - |
| dc.title | Calculating different weights in feature values in logistic regression | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1145/3018009.3018017 | - |
| dc.identifier.scopusid | 2-s2.0-85014956564 | - |
| dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series, pp 148 - 150 | - |
| dc.citation.title | ACM International Conference Proceeding Series | - |
| dc.citation.startPage | 148 | - |
| dc.citation.endPage | 150 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordAuthor | Classification | - |
| dc.subject.keywordAuthor | Feature Weighting | - |
| dc.subject.keywordAuthor | Logistic Regression | - |
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