On the Use of Adaptive Weights for the F_infty-Norm Support Vector Machine
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 방성완 | - |
dc.contributor.author | 전명식 | - |
dc.date.accessioned | 2021-09-07T00:46:46Z | - |
dc.date.available | 2021-09-07T00:46:46Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 1225-066X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/109598 | - |
dc.description.abstract | When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_\infty$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_\infty$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_\infty$-norm ($\text{AF}_\infty$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_\infty$-norm penalty. The $\text{AF}_\infty$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_\infty$-norm SVM. The simulation studies show that the proposed $\text{AF}_\infty$-norm SVM improves upon the $F_\infty$-norm SVM in terms of classification accuracy and factor selection performance. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국통계학회 | - |
dc.title | On the Use of Adaptive Weights for the F_infty-Norm Support Vector Machine | - |
dc.title.alternative | On the Use of Adaptive Weights for the F_infty-Norm Support Vector Machine | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 전명식 | - |
dc.identifier.bibliographicCitation | 응용통계연구, v.25, no.5, pp.829 - 835 | - |
dc.relation.isPartOf | 응용통계연구 | - |
dc.citation.title | 응용통계연구 | - |
dc.citation.volume | 25 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 829 | - |
dc.citation.endPage | 835 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001707206 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Adaptive weight | - |
dc.subject.keywordAuthor | $F_\infty$-norm penalty | - |
dc.subject.keywordAuthor | factor selection | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | support vector machine. | - |
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