Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations-Reference-Cited by-同舟云学术

Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations

Published:2019 Issue: Volume: Page:529-540
ISSN:0302-9743
Container-title:Mathematical Optimization Theory and Operations Research
language:
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Author:

Berikov Vladimir^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-22629-9_37

Reference14 articles.

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3. Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 912–919 (2003)

4. Avrachenkov, K., Mishenin, A., Goncalves, P., Sokol, M.: Generalized optimization framework for graph-based semi-supervised learning. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 966–974 (2012)

5. Boongoen, T., Iam-On, N.: Cluster ensembles: a survey of approaches with recent extensions and applications. Comput. Sci. Rev. 28, 1–25 (2018)

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