Few-shot learning for short text classification-Reference-Cited by-同舟云学术

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Few-shot learning for short text classification

Published:2018-02-21 Issue:22 Volume:77 Page:29799-29810
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Yan Leiming,Zheng Yuhui,Cao Jie

Funder

National Natural Science Foundation of China

National Social Science Foundation of China

Jiangsu Provincial 333 Project

Six Major Talents PeakProject of Jiangsu Province

Priority Academic Program Development of Jiangsu Higher Education Institutions

Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/article/10.1007/s11042-018-5772-4/fulltext.html

Reference50 articles.

1. Bin G, Sheng VS (2016) A robust regularization path algorithm for ν-support vector classification. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2016.2527796

2. Bin G, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416

3. Blaes S, Burwick T (2017) Few-shot learning in deep networks through global prototyping[J]. Neural Netw Off J Int Neural Netw Soc 94:159–172

4. Chen B, Qi X, Sun X, Shi Y-Q (2017) Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection. J Vis Commun Image Represent

5. Cheng J, Zhang X, Li P et al (2016) Exploring sentiment parsing of microblogging texts for opinion polling on Chinese public figures. Appl Intell 45(2):429–442

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