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Bridging domains using world wide knowledge for transfer learning
Abstract A major problem of classification learningA major problem of classification learning is the lack of ground-truth labeled data. It is usually expensive to label new data instances for training a model. To solve this problem, domain adaptation in transfer learning has been proposed to classify target domain data by using some other source domain data, even when the data may have different distributions. However, domain adaptation may not work well when the differences between the source and target domains are large. In this paper, we design a novel transfer learning approach, called BIG (Bridging Information Gap), to effectively extract useful knowledge in a worldwide knowledge base, which is then used to link the source and target domains for improving the classification performance. BIG works when the source and target domains share the same feature space but different underlying data distributions. Using the auxiliary source data, we can extract a bridge that allows cross-domain text classification problems to be solved using standard semisupervised learning algorithms. A major contribution of our work is that with BIG, a large amount of worldwide knowledge can be easily adapted and used for learning in the target domain. We conduct experiments on several real-world cross-domain text classification tasks and demonstrate that our proposed approach can outperform several existing domain adaptation approaches significantly.omain adaptation approaches significantly.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion By conducting experiments on different difBy conducting experiments on different difficult domain adaptation tasks, we show that our algorithm can significantly outperform several existing domain adaptation approaches in situations when the source and target domains are far from each other. In each case, an auxiliary domain can be used to fill in the information gap efficiently. We make three major contributions in this paper. 1) Instead of the traditional instance-based or feature-based perspective to view the problem of domain adaptation, we view the problem from a new perspective, i.e., we consider the problem of transfer learning as one of filling in the information gap based on a large document corpus. We show that we can obtain useful information to bridge the source and the target domains from auxiliary data sources. 2) Instead of devising new models for tackling the domain adaptation problems, we show that we can successfully bridge the source and target domains using well developed semisupervised learning algorithms. 3) We propose a minmargin algorithm that can effectively identify and reduce the information gap between two domains.e the information gap between two domains.
Data source Experiment responses  + , Wikipedia pages  +
Doi 10.1109/TKDE.2010.31 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Bridging%2Bdomains%2Busing%2Bworld%2Bwide%2Bknowledge%2Bfor%2Btransfer%2Blearning%22  +
Has author Evan Wei Xiang + , Bin Cao + , Derek Hao Hu + , Qiang Yang +
Has domain Computer science +
Has topic Text classification +
Issue 6  +
Pages 770-783  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in IEEE Transactions on Knowledge and Data Engineering +
Research design Experiment  + , Mathematical modeling  + , Statistical analysis  +
Research questions In this paper, we design a novel transfer In this paper, we design a novel transfer learning approach, called BIG (Bridging Information Gap), to effectively extract useful knowledge in a worldwide knowledge base, which is then used to link the source and target domains for improving the classification performance. BIG works when the source and target domains share the same feature space but different underlying data distributions. Using the auxiliary source data, we can extract a “bridge” that allows cross-domain text classification problems to be solved using standard semisupervised learning algorithms. A major contribution of our work is that with BIG, a large amount of worldwide knowledge can be easily adapted and used for learning in the target domain.nd used for learning in the target domain.
Revid 10,688  +
Theories Undetermined
Theory type Design and action  +
Title Bridging domains using world wide knowledge for transfer learning
Unit of analysis Article  +
Url http://dx.doi.org/10.1109/TKDE.2010.31  +
Volume 22  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Dump  +
Wikipedia language Not specified  +
Wikipedia page type Article  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:24:27  +
Categories Text classification  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:21:13  +
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