By Topic

The Transfer Learning Based on Relationships between Attributes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jinwei Zhao ; Dept. of Comput. Sci., Xi'an Jiaotong Univ., Xi'an, China ; Boqin Feng ; Guirong Yan ; Longlei Dong

In practical engineering, small-scale data sets are usually sparse and contaminated by noise. It is difficult to guarantee a competitive generalization performance of regression model from such a data set. However, what is worth mentioning is that there are often a lot of incomplete relationships between attributes in practical engineering. The involvement of the relationships might be significant in improving the generalization performance of machine learning. So in this paper, we propose a transfer learning method based on the incomplete relationships between attributes, in which the incomplete relationships is reasoned to get complete relationships, and the complete relationships are then transferred to the regression learning to improve the generalization performance of the regression model. Finally the proposed method was applied to least squares support vector machine (LSSVM) and was evaluated on benchmark data sets. The experiment results show that the transfer learning can improve the generalization performance and prediction accuracy of the regression model.

Published in:

Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on

Date of Conference:

10-10 Dec. 2012