- 期刊
A Robust Fuzzy Support Vector Machine for Two-class Pattern Classification
並列摘要
This paper proposes a systematic method to classify data with outliers. The essential techniques consist of the outlier detection and the fuzzy support vector machine (FSVM). In this approach, the main body set for each class is first determined by the outlier detection algorithm (ODA) that estimates the outliers based on the total similarity objective function. Then, incorporated with the total similarity measure of the ODA, a fuzzy membership degree is assigned to each training sample. Experiments show that the proposed method can greatly reduce the effects of outliers in the training process and the final decision surface of the FSVM is insensitive to outliers.
被引用紀錄
Chen, Y. H. (2018). 群組交易策略組合最佳化技術之研究 [master's thesis, Tamkang University]. Airiti Library. https://doi.org/10.6846/TKU.2018.00196
Chou, H. (2017). 具隸屬函數調整機制的模糊時序關聯規則萃取技術 [master's thesis, Tamkang University]. Airiti Library. https://doi.org/10.6846/TKU.2017.00082
林益誠（2012）。應用有限元素頻域分析探討多孔樑與多孔結構耦合之脈衝響應〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2012.00265
陳勇昇（2012）。應用複合多評準決策與模糊積分法探討台北都會區聯營公車服務品質〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2012.00097
Tseng, H. W. (2010). 前瞻無線寬頻系統之效能評估及研析 [doctoral dissertation, Tamkang University]. Airiti Library. https://doi.org/10.6846/TKU.2010.00588
延伸閱讀
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