Wenyuan Dai, Yuqiang Chen, Qiang Yang, Yong Yu
This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the...
Wenyuan Dai, Qiang Yang, Gui-rong Xue, Yong Yu
This paper focuses on a new clustering task, called self-taught clustering. Self-taught clustering is an instance of unsupervised transfer learning, which aims at clustering a small collection of...
ABSTRACT Spectral Domain-Transfer Learning (2009)
Xiao Ling, Wenyuan Dai, Gui-rong Xue, Qiang Yang, Yong Yu
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world applications, however,...
Topic-bridged PLSA for Cross-Domain Text Classification (2009)
Gui-rong Xue, Wenyuan Dai, Qiang Yang, Yong Yu
In many Web applications, such as blog classification and newsgroup classification, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and...
Research Track Paper Co-clustering based Classification for Out-of-domain Documents ABSTRACT (2008)
Wenyuan Dai, Gui-rong Xue, Qiang Yang, Yong Yu
In many real world applications, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled...
TrAdaBoost = Transfer AdaBoost Experimental Results Conclusion Boosting for Transfer Learning (2008)
Can chinese web pages be classified with english data source (2008)
Xiao Ling, Gui-rong Xue, Wenyuan Dai, Yun Jiang, Qiang Yang, Yong Yu
As the World Wide Web in China grows rapidly, mining knowledge in Chinese Web pages becomes more and more important. Mining Web information usually relies on the machine learning techniques which...
Transferring naive bayes classifiers for text classification (2007)
Wenyuan Dai, Gui-rong Xue, Qiang Yang, Yong Yu
A basic assumption in traditional machine learning is that the training and test data distributions should be identical. This assumption may not hold in many situations in practice, but we may be...
Boosting for transfer learning (2007)
Wenyuan Dai, Qiang Yang, Gui-rong Xue, Yong Yu
Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumption does not hold....