2010.09 - 2015.06
Pattern Recognition and Intelligence System,
Department of Computer Science and
University of Science and Technology,
Scholar in Department of Electrical Engineering, Idaho State
University, Pocatello, U.S.
Visiting Scholar in Department of Computer
Science and Engineering, University of Notre Dame, South Bend, U.S.
2006.09 - 2010.06
B.Sc., in Mathematics,
University of Science and Technology, Nanjing, China
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I have broad interests in pattern recognition, machine
learning and computer vision. Currently, I am interested
in the following topics and related application areas.
Active Learning, Semi-supervised Learning
Environmental Perception, Terrain classification
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Yingjie Gu, Zhong Jin. Neighborhood Preserving D-Optimal Design
for Active Learning and Its Application to
Terrain Classification. Neural Computing and Applications, 2013,
Zhong Jin, and Steve C. Chiu. Active Learning Combining
Uncertainty and Diversity for Multi-Class Image Classification.
IET Computer Vision, 2015, 9(3): 400-407.
Yingjie Gu, Zhong Jin. Terrain Classification based on Visible
and Infrared Data Fusion. Computer Engineering, 2013, 39(2):
Zhong Jin, and Steve C. Chiu. Active Learning with Maximum
Density and Minimum Redundancy. International Conference on
Neural Information Processing (ICONIP). 2014: 103-110.
Zhong Jin, and Steve C. Chiu. Combining Active Learning and
Semi-Supervised Learning Using Local and Global Consistency.
International Conference on Neural Information Processing (ICONIP).
Yingjie Gu, Zhong Jin.
Grass Detection Based on Color Features. CCPR2010, ChongQing,
2010, 2: 687-691. (Chinese)
Yingjie Gu, Dawid Zydek, and Zhong Jin.
Active Learning Based on Random Forest and Its Application to
Terrain Classification. International Conference on Systems
Engineering. 2015: 273-278.
Yingjie Gu and Dawid Zydek. Active Learning for Intrusion
Detection. National Wireless Research Collaboration Symposium (NWRCS).
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resources for research:
Machine Learning Resources
Machine Learning Open Source Software
LIBSVM -- A Library for Support Vector Machines
Semi-Supervised Learning Software
RandomForest: Breiman and Cutler's random forests for
classification and regression
Fast l-1 Minimization Algorithms: Homotopy and Augmented
GIST Descriptor (Matlab code)
Paper Submission Instruction
Groups & Scholars
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