基本信息：
姓名：可婷
性别：女
职称：讲师
电子邮箱：keting@tust.edu.cn
联系电话：**
通讯地址：天津经济技术开发区第十三大街29号天津科技大学西校区8号楼
研究方向：机器学习，模式识别，数据挖掘，支持向量机，最优化理论与算法
招生专业：
（1）院系名称：人工智能学院
招生类别：硕士
学位类型：专业学位
专业大类：工学
一级学科：电子信息
专业名称：软件工程
类别：全日制
代表性论文：
[1] Ting Ke, Hui Lv, Mingjing Sun, Lidong Zhang. A Biased Least Square Support Vector Machine Based on Mahalanobis Distance for PU Learning[J]. Physical A: Statistical Mechanics and its Applications. 2018, 509: 422-438. (SCI)
[2] Ting Ke, Ling Jing, Hui Lv, Lidong Zhang, Yaping Hu. Global and Local Learning from Positive and Unlabeled Examples[J]. Applied Intelligence. 2018, 48(8): 2373-2392. (SCI)
[3] Ting Ke, Min Li, Lidong Zhang, Hui Lv, Xuechun Ge. Construct a Biased SVM Classifier Based on Chebyshev Distance for PU Learning[J]. Journal of Intelligent & Fuzzy Systems. 2020，DOI 10.3233/IFS-192064. (SCI）
[4] 可婷，葛雪纯，张立东，吕慧，铁路道岔故障的智能诊断.电子技术应用, 2020, 46(4): 29-33.
[5] Ting Ke, Lujia Song, Bing Yang, Xinbin Zhao, Ling Jing. Building a Biased Least Squares Support Vector Machine Classifier for Positive and Unlabeled Learning[J]. Journal of software. 2014, 9(6): 1494-1502. (EI)
[6] Ting Ke, Junyan Tan, Bing Yang, Li Yi, Ling Jing. A Novel Graph-based Approach for Transductive Positive and Unlabeled Learning[J]. Journal of computational information systems, 2014, 10 (4): 1439-1447. (EI)
[7] Ting Ke, Bing Yang, Ling Zhen, Junyan Tan, Yi Li, Ling Jing. Building High-performance Classifiers on Positive and Unlabeled Examples for Text Classification. [100] Advances in Neural Networks – ISNN 2012，Lecture Notes in Computer Science Volume 7368, 2012, 187-195. (EI)
[8] Zhiqiang Zhang, Ting Ke, Naiyang Deng, Junyan Tan. Biased p-norm Support Vector Machine for PU Learning[J]. Neurocomputing, 2014, 136: 256-261. (SCI)
[9] Jingjing Qiang, Ting Ke, Bing Yang, Ling Jing. Building SVM Classifier Based on Posterior Probability Using Positive and Unlabeled Examples[J]. International Journal of Digital Content Technology and its Applications, 2012, 6(23): 289-297. (EI)
[10] Lujia Song, Bing Yang, Ting Ke, Ling Jing. Biased Locality-sensitive Support Vector Machine Based on Density for Positive and Unlabeled Examples Learning[100]. 11th International Symposium on Operations Research and its Applications in Engineering, Technology and Management 2013 (ISORA 2013), 2013, 1-6.
科研项目：
1.天津市教委科研计划项目，2018KJ115，基于支持向量机的铁路道岔故障的智能诊断
2019/01-2021/01，6万元，在研，主持。
2.教育部人文社科项目，基于矩匹配技术的多维随机波动率金融市场下保险公司投
资、再保险策略研究，2020/1-2022/12，8万，在研，第二。
其他（获奖成果、专利）等
1.可婷，吕慧，张立东，刘寅立，李敏. 铁路道岔故障的智能检测方法: 中国，8.0 [P]. 2019-09-11.