“Sunwei”的版本间的差异
来自南京大学IIP
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*I am interested in Machine Learning and Multi-label Learning. | *I am interested in Machine Learning and Multi-label Learning. | ||
+ | *Specifically, now my first work is to exploit label correlations on multi-label learning, i.e. On Multi-label Text Classification (MLTC), text features can be regarded as detailed description of documents and label sets can be a summarization of documents. Hybrid Topics from text features and label sets by LDA (a method of topic model) can effectively represent the whole label correlations. | ||
<span style="font-size:larger;"><span style="color:#3498db;">'''Resources'''</span></span> | <span style="font-size:larger;"><span style="color:#3498db;">'''Resources'''</span></span> |
2018年10月30日 (二) 21:04的版本
M.Sc. Student @ IIP Group Email: weisun_@outlook.com | |
Supervisor
- Professor Jun-Yuan Xie
Biography
- I received my B.Sc. degree in of Soochow University in June 2017. In the same year, I was admitted to study for a Master degree in Nanjing University without entrance examination. Currently I am a second year M.Sc. student of Department of Computer Science and Technology in Nanjing University and a member of IIP Group, led by professor Jun-Yuan Xie and Chong-Jun Wang.
Research Interest
- I am interested in Machine Learning and Multi-label Learning.
- Specifically, now my first work is to exploit label correlations on multi-label learning, i.e. On Multi-label Text Classification (MLTC), text features can be regarded as detailed description of documents and label sets can be a summarization of documents. Hybrid Topics from text features and label sets by LDA (a method of topic model) can effectively represent the whole label correlations.
Resources
- Extreme Classification Repository: for large-scale multi-label datasets and off-the-shelf eXtreme Multi-Label Learning (XML) solvers.
- Mulan Multi-Label Learning Datasets: regular/traditional multi-label learning datasets.
- Related Works: This page categorizes a list of works of my interest, mainly in Multi-Label Learning.
Rewards or Honors
- Second-Class Academic Scholarship, 2018-2019
- First-Class Academic Scholarship, 2017-2018