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Research Article

An Effective Active Semi-supervised Learning Method Based on Manifold Regularization

Xiukuan Zhao, Baiqi Ning and Gangbing Song

Conventional artificial intelligent methods such as neural network and SVM use only labeled data (feature/label pairs) for training. Labeled instances are often difficult, expensive, or time consuming to obtain. To use a large amount of unlabeled data together with labeled data to build better models, we proposed an active semi-supervised learning method based on the pool query active learning and manifold regularization semi-supervised method. In this paper, the effectiveness of the method was verified by its application to a synthetic data set and three real world classification problems. The experimental results showed that employing our active semi-supervised learning method can significantly reduce the need for labeled training instances.

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  How to cite this article:

Xiukuan Zhao, Baiqi Ning and Gangbing Song, 2013. An Effective Active Semi-supervised Learning Method Based on Manifold Regularization. Journal of Applied Sciences, 13: 1728-1733.

DOI: 10.3923/jas.2013.1728.1733


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