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論文
【年度】103
年研發成果
【項目】
論文
【領域】
軍品釋商科專
【類別】
機電運輸
| 計畫名稱 | 機械與運輸領域-軍品科技創新應用與釋商計畫 | | 論文名稱 | Using Max- min Range Method for Rasing the Accuracy of One-class Classifiers | | 論文類型 | 研討會 | | 發表處 | 第五屆國際先進製造技術研討會(ICAM 2014, International Conference on Advanced Manufacturing) | | 發表人 | Chi-Kai Wang (王啟凱)、Wen-Hao Tseng( 曾文豪)、Jia-Jyh Yan( 顏家智)、Chorng-Shyan Lin(林崇賢) | | 發表日期 | 2014/09/30 | | 國家 | 國內 | | 內容摘要 | One of the important technologies for unmanned vehicles is image processing which feature identification is its kernel. The support vector data paper1list_paper1list_paper1list_description111 (SVDD) is a method proposed to solve the problem of one-class classification. It models a hypersphere around the target set, and by the introduction of kernel functions, more flexible paper1list_paper1list_paper1list_description111s are obtained. In SVDD, the width parameter s and the penalty parameter c have to be given beforehand by the user. To automatically optimize the values for these parameters, the error on both the target and outlier data has to be estimated. Because no outlier examples are available, we propose a max-min range method for generating artificial outliers in this paper. By generating artificial outliers around the target set, the accuracy of classifiers will improve. At the last, we use four benchmark data sets: Iris, Wine, Balance-scale, and Ionosphere data base to validate the approach in this research indeed has better classification result. |
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