Shiting Wen

Learn More
Most of the existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-source(More)
In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we(More)
Traditional service selection schemes require users to define a utility function by assigning weights to each quality-of-service (QoS) metric. To relieve users from the professional knowledge, skyline techniques have been studied recently by several researchers. However, the size of skyline services is sometimes not easy controlled due to intrinsic(More)
In dynamic Web service environments, the performance of the Internet is unpredictable; the reliability and effectiveness of remote Web services are also unclear. Therefore, it can hardly be guaranteed that the quality of Web service (QoWS) attributes of Web services do not fluctuate with the dynamic Web service environments. When a composite service is(More)