Spatially adaptive binary classifier using B-splines and total variation penalty
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bak, Kwan-Young | - |
dc.contributor.author | Jhong, Jae-Hwan | - |
dc.contributor.author | Koo, Ja-Yong | - |
dc.date.accessioned | 2021-09-01T04:43:52Z | - |
dc.date.available | 2021-09-01T04:43:52Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-10-02 | - |
dc.identifier.issn | 1048-5252 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/62551 | - |
dc.description.abstract | This paper reports on our study of a binary classifier based on B-splines and the total variation penalty. The decision boundary of the proposed classifier is obtained using a variant of the hinge loss function. We restrict our focus to a two-dimensional predictor space to analyse the theoretical behaviour of the spline decision curve estimator. Theoretical investigation shows that the proposed estimator achieves the same optimal rate of convergence as in nonparametric regression estimation under some regularity conditions. The proposed method is implemented with a coordinate descent algorithm. Numerical studies using real and simulated data are conducted to complement the theoretical results. The results show that the proposed estimator adapts well to the data and yields more accurate predictions than other existing support vector machine methods. We also discuss directions for future research. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Spatially adaptive binary classifier using B-splines and total variation penalty | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Koo, Ja-Yong | - |
dc.identifier.doi | 10.1080/10485252.2019.1663847 | - |
dc.identifier.scopusid | 2-s2.0-85073672963 | - |
dc.identifier.wosid | 000485351800001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF NONPARAMETRIC STATISTICS, v.31, no.4, pp.887 - 910 | - |
dc.relation.isPartOf | JOURNAL OF NONPARAMETRIC STATISTICS | - |
dc.citation.title | JOURNAL OF NONPARAMETRIC STATISTICS | - |
dc.citation.volume | 31 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 887 | - |
dc.citation.endPage | 910 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordAuthor | Binary classification | - |
dc.subject.keywordAuthor | convergence rate | - |
dc.subject.keywordAuthor | decision curve | - |
dc.subject.keywordAuthor | splines | - |
dc.subject.keywordAuthor | total variation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.