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SVM-Guided Biplot of Observations and Variables
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 허명회 | - |
| dc.date.accessioned | 2021-09-06T09:37:04Z | - |
| dc.date.available | 2021-09-06T09:37:04Z | - |
| dc.date.created | 2021-06-17 | - |
| dc.date.issued | 2013 | - |
| dc.identifier.issn | 2287-7843 | - |
| dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/105835 | - |
| dc.description.abstract | We consider support vector machines(SVM) to predict Y with p numerical variables X_1,..., X_p. This paper aims to build a biplot of $p$ explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori. | - |
| dc.language | English | - |
| dc.language.iso | en | - |
| dc.publisher | 한국통계학회 | - |
| dc.title | SVM-Guided Biplot of Observations and Variables | - |
| dc.title.alternative | SVM-Guided Biplot of Observations and Variables | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | 허명회 | - |
| dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.20, no.6, pp.491 - 498 | - |
| dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
| dc.citation.title | Communications for Statistical Applications and Methods | - |
| dc.citation.volume | 20 | - |
| dc.citation.number | 6 | - |
| dc.citation.startPage | 491 | - |
| dc.citation.endPage | 498 | - |
| dc.type.rims | ART | - |
| dc.identifier.kciid | ART001820374 | - |
| dc.description.journalClass | 2 | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.subject.keywordAuthor | Support vector machine | - |
| dc.subject.keywordAuthor | kernel trick | - |
| dc.subject.keywordAuthor | principal component analysis | - |
| dc.subject.keywordAuthor | biplot. | - |
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