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Cross‐project defect prediction (CPDP) technology refers to the constructing prediction model to predict the instance label of the target project by utilising labelled data from an external project. The challenge of CPDP methods is the distribution ...

Unsupervised domain adaption aims to reduce the divergence between the source domain and the target domain. The final objective is to learn domain‐invariant features from both domains that get the minimised expected error on the target domain. The ...

This study addresses the task of supervised cross‐domain image generation, which aims to translate an image from the source domain to the target domain, guided by a reference image from the latter. The key difference between the authors setting and the ...

This study presents a transfer learning method for addressing the insufficient sample problem in hyperspectral image classification. In order to find common feature representation for both the source domain and target domain, we introduce a regularisation ...

As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering ...

Learning to rank arises in many information retrieval applications, ranging from Web search engine, online advertising to recommendation systems. Traditional ranking mainly focuses on one type of data source, and effective modeling relies on a ...

Learning to rank arises in many information retrieval applications, ranging from Web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking function heavily depends on the number of labeled examples ...

Supervised learning algorithms usually require high quality labeled training set of large volume. It is often expensive to obtain such labeled examples in every domain of an application. Domain adaptation aims to help in such cases by utilizing data ...

We deal with modular mappings as introduced by H.J. Lee and J.A.B. Fortes (1994) and we build upon their results. Our main contribution is a characterization of one to one modular mappings that is valid even when the source domain and the target domain ...