<学術雑誌論文>
NOVEL WRAPPER APPROACH FOR VARIABLE SELECTION IN SUPPORT VECTOR MACHINE

作成者
本文言語
出版者
発行日
収録物名
開始ページ
終了ページ
出版タイプ
アクセス権
Crossref DOI
概要 Support vector machine (SVM) is an efficient machine learning method for classification. In this paper, we propose two variable selection criteria for SVM that use wrapper methods. The criteria measur...e the contribution of each variable for a target function. The variable importance is quantified on the basis of the measured amount. The methods have high computational efficiency because they evaluate the importance of all variables without recursive calculations. They were applied to several artificial and real-world data sets, and their results were superior to those of existing methods.続きを見る

本文情報を非表示

7-Koda pdf 633 KB 51  

詳細

PISSN
EISSN
NCID
レコードID
主題
助成情報
登録日 2019.02.26
更新日 2020.10.22

この資料を見た人はこんな資料も見ています