Improving Non-Negative Positive-Unlabeled Learning for News Headline Classification-Reference-Cited by-同舟云学术

Improving Non-Negative Positive-Unlabeled Learning for News Headline Classification

Author:

Ji Zhanlin¹^ORCID,Du Chengyuan¹,Jiang Jiawen¹,Zhao Li²,Zhang Haiyang³,Ganchev Ivan⁴^ORCID

Affiliation:

1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China

2. Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Tsinghua University, Beijing, China

3. Department of Computing, Xi’an Jiaotong--Liverpool University, Suzhou, China

4. Telecommunications Research Centre (TRC), University of Limerick, Limerick, Ireland

Funder

National Key Research and Development Program of China

Tsinghua Precision Medicine Foundation

Science and Education for Smart Growth Operational Program

European Union through the European Structural and Investment

Telecommunications Research Centre (TRC), University of Limerick, Ireland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference34 articles.

1. Partially supervised classification of text documents;liu;Proc ICML,2002

4. Novelty detection: Unlabeled data definitely help;scott;Proc Artif Intell Statist,2009

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