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导师信息——可婷


发布日期:2020-07-24

威尼斯游戏大厅

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:18222286928

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:威尼斯游戏大厅

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,201910857428.0 [P]. 2019-09-11.