transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. feature selection for transfer learning (f-mmd) is a simple and effective transfer learning method, which tackles the domain shift problem. f-mmd has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive perf...

The initial lower and upper power domain constructions P _ and P ^ commute under composition for all cpos. The common result P _ (P ^ X) and P ^ (P _ X) is the free frame over the cpo X.

Stably compact spaces Xs can be described as being derived from compact ordered spaces X by weakening their topology to the open upper sets. In this paper the probabilistic powerdomain of a stably compact space Xs is investigated using the compact ordered space X and classical tools from measure theory and functional analysis. This allows to derive in a unified and elegant way known and new res...

We describe a sentiment classification method that is applicable when we do not have any labeled data for a target domain but have some labeled data for multiple other domains, designated as the source domains. We automatically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to find the association between words that express similar sent...

Transfer learning has benefited many real-world applications where labeled data are abundant in source domains but scarce in the target domain. As there are usually multiple relevant domains where knowledge can be transferred, multiple source transfer learning (MSTL) has recently attracted much attention. However, we are facing two major challenges when applying MSTL. First, without knowledge a...

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source (SUDA), shift MUDA exists not only between the source and target domains but also among multiple domains. Most existing algorithms focus on extracting domain-invariant representations all whereas task-specific decision boundaries classes are largely negle...