Subscribe Now Subscribe Today
Science Alert
 
FOLLOW US:     Facebook     Twitter
Blue
   
Curve Top
Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 10 | Page No.: 1728-1733
DOI: 10.3923/jas.2013.1728.1733
An Effective Active Semi-supervised Learning Method Based on Manifold Regularization
Xiukuan Zhao, Baiqi Ning and Gangbing Song

Abstract:
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.
PDF References Citation Report Citation
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

URL: https://scialert.net/abstract/?doi=jas.2013.1728.1733

 
COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 

       

       

Curve Bottom