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- articleAugust 2014
Multi-transfer: Transfer learning with multiple views and multiple sources
Statistical Analysis and Data Mining (STADM), Volume 7, Issue 4Pages 282–293https://doi.org/10.1002/sam.11226Transfer learning, which aims to help learning tasks in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications such as text mining, sentiment analysis, and so on. In addition, in ...
- ArticleNovember 2012
A machine learning approach for instance matching based on similarity metrics
ISWC'12: Proceedings of the 11th international conference on The Semantic Web - Volume Part IPages 460–475https://doi.org/10.1007/978-3-642-35176-1_29The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances ...
- short-paperSeptember 2012
Constrained collective matrix factorization
RecSys '12: Proceedings of the sixth ACM conference on Recommender systemsPages 237–240https://doi.org/10.1145/2365952.2366003Transfer learning for collaborative filtering (TLCF) aims to solve the sparsity problem by transferring rating knowledge across multiple domains. Taking domain difference into ac- count, one of the issues in cross-domain collaborative filtering is to ...
- research-articleJune 2010
Bridging Domains Using World Wide Knowledge for Transfer Learning
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 22, Issue 6Pages 770–783https://doi.org/10.1109/TKDE.2010.31A 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 ...
