Optimizing the class information divergence for transductive classification of texts using propagation in bipartite graphs-Reference-Cited by-同舟云学术

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Optimizing the class information divergence for transductive classification of texts using propagation in bipartite graphs

Published:2017-02 Issue: Volume:87 Page:127-138
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

de Paulo Faleiros Thiago,Geraldeli Rossi Rafael,de Andrade Lopes Alneu

Funder

São Paulo Research Foundation (FAPESP)

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference34 articles.

1. Towards graphical models for text processing;Aggarwal;Knowl. Inf. Syst.,2013

2. Graph-based text classification: learn from your neighbors;Angelova,2006

3. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples;Belkin;J. Mach. Learn. Res.,2006

4. Graph construction based on labeled instances for semi-supervised learning;Berton,2014

5. Combining labeled and unlabeled data with co-training;Blum,1998

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Coarsening effects on k-partite network classification;Applied Network Science;2023-12-05

2. Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks;Frontiers in Research Metrics and Analytics;2022-06-16

3. A review and comparative analysis of coarsening algorithms on bipartite networks;The European Physical Journal Special Topics;2021-06-07

4. A Critical Survey of the Multilevel Method in Complex Networks;ACM Computing Surveys;2021-03-31

5. Unsupervised learning of textual pattern based on Propagation in Bipartite Graph;Intelligent Data Analysis;2020-05-21

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