頁籤選單縮合
題名 | Twin-SVDD Classifier with the Conception of Relative Distance= |
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作者 | Yang, Lei; Ma, Wei-min; Tian, Bo; |
期刊 | Journal of Management Science & Statistical Decision |
出版日期 | 20100300 |
卷期 | 7:1 2010.03[民99.03] |
頁次 | 頁110-117 |
分類號 | 448.6 |
語文 | eng |
關鍵詞 | Support vector domain description; Twin support vector domain description; Relative distance; Pattern classification; |
英文摘要 | Twin support vector domain description(Twin-SVDD) classifier with the conception of relative distance was proposed in this paper. The preliminary SVDD model described the target dataset with one class by constructing a compact optimized hypersphere in feature space. And the model was effective to deal with problems of pattern classification with imbalanced dataset such as outlier detection. But only information about the positive class of dataset was used in the preliminary SVDD model. As for binary classification problem, inspired by the construction of Twin support vector machine where nonparallel planes were solved separately, the Twin-SVDD model was proposed. Two optimized hyperspheres which described positive and negative class of datasets were constructed separately in the Twin-SVDD model. So information about both classes of dataset was used. And then new classification decision-making function was constructed based on the parameters of the Twin-SVDD model with the conception of relative distance. At last, experiments were performed. Experimental results showed that the Twin-SVDD model was more effective than the preliminary SVDD model when dealing with pattern classification problems. And the proved classification decision-making function improved the performance of the Twin-SVDD model. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。