A Semi-Supervised Self-Training Ensemble Method Based on Clustering
Author:
Affiliation:
1. College of big data and intelligent engineering, Southwest Forestry University,Kunming,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10065600/10065601/10066026.pdf?arnumber=10066026
Reference28 articles.
1. Multi-label classification of documents using fine-grained weights and modified co-training
2. Semi-supervised learning by disagreement[J];hua;Knowledge and Information Systems,2010
3. GAR: An efficient and scalable graph-based activity regularization for semi-supervised learning
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