A Purity Measure Based Transductive Learning Algorithm-Reference-Cited by-同舟云学术

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A Purity Measure Based Transductive Learning Algorithm

Published:2013 Issue: Volume: Page:405-412
ISSN:0302-9743
Container-title:Advances in Neural Networks – ISNN 2013
language:
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Author:

Bertini Junior João Roberto,Zhao Liang

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-39068-5_49

Reference14 articles.

1. Bertini Jr., J.R., Zhao, L., Motta, R., Lopes, A.A.: A Nonparametric Classification Method based on K-associated Graphs. Inform. Sciences 181, 5435–5456 (2011)

2. Bertini Jr., J.R., Lopes, A.A., Zhao, L.: Partially Labeled Data Stream Classification with the Semi-supervised K-associated Graph. J. Brazil. Comp. Soc. 18, 299–310 (2012)

3. Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-training. In: Proc. 11th Annual Conf. on Computational Learning Theory, pp. 92–100 (1998)

4. Chapelle, O., Zien, A., Schölkopf, B. (eds.): Semi-supervised Learning. MIT Press (2006)

5. Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms. MIT Press (2009)

Cited by 1 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. A Comparison of Two Purity-Based Algorithms When Applied to Semi-supervised Streaming Data Classification;2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence;2013-09

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