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周 博

2019年01月04日 14:26  点击:[]


   周   博   讲 师

   工作地点:财经主楼 613#

   邮    箱bozhou@xjtu.edu.cn

教育经历:

     2003.9-2007.7 南京大学,软件工程学士学位。

     2007.9-2009.9 日本早稻田大学, IPS(Information, Production and system)大学院,信息方向硕士学位。

     2009.9-2018.9 日本早稻田大学, IPS(Information, Production and system)大学院, 工学博士学位。

主要研究课题:

   支持向量机(SVM)及核方法

   半监督学习

   深度学习

     机器学习方法在经济学领域的应用

发表论文情况:

杂志论文发表(五篇SCI 检索):

1.B. Zhou, B. Chen and J. Hu, "Quasi-linear Support Vector Machine for Nonlinear Classification",

IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences,Vol.E97-A,

No.7, July, pp.1587-1594, 2014.

2. W. Li,B. Zhou, B. Chen and J. Hu, "A Deep Neural Network Based Quasi-linear Kernel for Support

Vector Machines",IEICE Trans. on Fundamentals of Electronics, Communications and Computer

Sciences,Vol.E99-A, No.12, Dec., 2016.

3. W.Li,B.Zhou, B.Chen and J.Hu, "A Geometry-Based Two-step Method for Nonlinear Classification

Using Quasi-Linear Support Vector Machine",IEEJ Trans. on Electrical and Electronic Engineering,

Vol.12, No.6, Nov., 2017.

4.B.Zhou, W.Li and J.Hu, "A New Segmented Oversampling Method for Imbalanced Data

Classification Using Quasi-Linear Support Vector Machine",IEEJ Trans. on Electrical and

Electronic Engineering,Vol.12, No.6, Nov., 2017.

5. B. Zhou, W. Li and J. Hu, "A Coarse-to-Fine Two-step Method for Semi-Supervised Classification

Using Quasi-Linear Laplacian SVM", submitted toIEEJ Trans. on Electrical and Electronic

Engineering.(Received)

会议论文发表:

1.B. Zhouand J. Hu, "A Dynamic Pattern Recognition Approach Based on Neural Network for Stock

Time-Series", inProc. of World Congress on Nature and Biologically Inspired Computing(NaBIC

2009) (India), 12, 2009, pp.1552-1555.

2.B. Zhou, C. Yang, H. Guo and J. Hu, "A Quasi-linear SVM Combined with Assembled SMOTE for

Imbalanced Data Classification", inProc. of 2013 IEEE International Joint Conference on Neural

Networks(IJCNN'2013) (Dallas), Aug., 2013, pp.2351-2357.

3. C. Hu,B. Zhou, and J. Hu, "Fast Support Vector Data Description Training Using Edge Detection

on Large Datasets", inProc. of 2014 IEEE International Joint Conference on Neural Networks

(IJCNN'2014) (Beijing), July, 2014, pp.2176-2182.(Best Student Paper Award - Finalist)

4.B. Zhou, C. Hu, B. Chen and J. Hu, "A Transductive Support Vector Machine with Adjustable

Quasi-linear Kernel for Semi-supervised Data Classification", inProc. of 2014 IEEE International

Joint Conference on Neural Networks(IJCNN'2014) (Beijing), July, 2014, pp.1409-1415.

5.B. Zhou, D. Fu, C. Dong and J. Hu, "A Transductive SVM with Quasi-linear Kernel Based on

Cluster Assumption for Semi-Supervised Classification", inProc. of 2015 IEEE International Joint

Conference on Neural Networks(IJCNN'2015) (Killarney), July, 2015.

6. D. Fu,B. Zhouand J. Hu, "Improving SVM Based Multi-label Classification by Using Label

Relationship", inProc. of 2015 IEEE International Joint Conference on Neural Networks

(IJCNN'2015) (Killarney), July, 2015.

7. C. Dong,B. Zhouand J. Hu, "A Hierarchical SVM Based Multiclass Classification by Using

Similarity Clustering", inProc. of 2015 IEEE International Joint Conference on Neural Networks

(IJCNN'2015) (Killarney), July, 2015.

8. W. Li,B. Zhouand J. Hu, "A Kernel Level Composition of Multiple Local Classifiers for Nonlinear

Classification", in Proc. of 2016 IEEE International Joint Conference on Neural Networks

(IJCNN'2016) (Vancouver), July, 2016, pp.3845-3850.

9. W. Li, B. Chen,B. Zhou, & J. Hu. A mixture of multiple linear classifiers with sample weight and

manifold regularization. In Proc. of 2017 IEEE International Joint Conference on Neural Networks

(IJCNN'2017), May, 2017. pp. 3747-3752.

项目经历及获奖情况:

1. 参与了国家自然科学青年基金项目“石油储层识别中软计算与硬计算融合的理论与方法研究”的研究工

作(项目编码:71103163, 主持人:郭海湘)。研究成果发表于2013 年国际神经网络大会,B. Zhou,

C. Yang, H. Guo and J. Hu, "A Quasi-linear SVM Combined with Assembled SMOTE for Imbalanced

Data Classification"。

2. 论文C. Hu, B. Zhou, and J. Hu, "Fast Support Vector Data Description Training Using Edge

Detection on Large Datasets" 获得2014 年国际神经网络大会最佳学生论文奖提名(大会共提名两篇)。

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