Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing
作者：
Highlights：
• A new semi-supervised support vector regression method is proposed.
• Label distribution is estimated by probabilistic local reconstruction algorithm.
• Different oversampling rate is used based on uncertainty information.
• Expected margin based pattern selection is used to reduce the training complexity.
• The proposed method improves the prediction performance with lower time complexity.
摘要
•A new semi-supervised support vector regression method is proposed.•Label distribution is estimated by probabilistic local reconstruction algorithm.•Different oversampling rate is used based on uncertainty information.•Expected margin based pattern selection is used to reduce the training complexity.•The proposed method improves the prediction performance with lower time complexity.
论文关键词：Semi-supervised learning,Support vector regression,Probabilistic local reconstruction,Data generation,Virtual metrology,Semiconductor manufacturing
论文评审过程：Received 7 September 2015, Revised 22 December 2015, Accepted 23 December 2015, Available online 4 January 2016, Version of Record 21 January 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.027
