Semi-supervised learning based on high density region estimation.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 20605081.
- Also identified by DOI 10.1016/j.neunet.2010.06.001.
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Abstract
In this paper, we consider local regression problems on high density regions. We propose a semi-supervised local empirical risk minimization algorithm and bound its generalization error. The theoretical analysis shows that our method can utilize unlabeled data effectively and achieve fast learning rate.
Medical subject headings
- Artificial Intelligence
- Computer Simulation
- Learning
- Neural Networks, Computer