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  Sharpened graph ensemble for semi-supervised learning
 
 
Title: Sharpened graph ensemble for semi-supervised learning
Author: Choi, Inae
Park, Kanghee
Shin, Hyunjung
Appeared in: Intelligent data analysis
Paging: Volume 17 (2013) nr. 3 pages 387-398
Year: 2013-05-21
Contents: The generalization ability of a machine learning algorithm varies on the specified values to the model parameters and the degree of noise in the learning dataset. If the dataset has an enough amount of labeled data points, the optimal value for the model parameter can be found via validation by using a subset of the given dataset. However, for semi-supervised learning – one of the most recent learning algorithms, this is not as available as in conventional supervised learning. In semi-supervised learning, it is assumed that the dataset is given with only a few labeled data points. Therefore, holding out some of labeled data points for validation is not easy. The lack of labeled data points, furthermore, makes it difficult to estimate the degree of noise in the dataset. To circumvent the addressed difficulties, we propose to employ ensemble learning and graph sharpening. The former replaces the model parameter selection procedure to an ensemble network of the committee members trained with various values of model parameter. The latter, on the other hand, improves the performance of algorithms by removing unhelpful information caused by noise. The experimental results demonstrate the applicability of the proposed method for many real-world problems with no concern for the technical difficulties, by selecting the best parameter values and mitigating the influence of noise.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 10 of 10 found articles
 
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