Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …

A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …

There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

Domain transfer learning, which learns a target classifier using labeled data from a different
distribution, has shown promising value in knowledge discovery yet still been a challenging …

Domain transfer learning generalizes a learning model across training data and testing data
with different distributions. A general principle to tackle this problem is reducing the …

Transfer learning is established as an effective technology to leverage rich labeled data from
some source domain to build an accurate classifier for the target domain. The basic …

Multi-task learning in text classification leverages implicit correlations among related tasks to
extract common features and yield performance gains. However, most previous works treat …

Previous research on online media popularity prediction concluded that the rise in popularity
of online videos maintains a conventional logarithmic distribution. However, recent studies …

There are many clustering tasks which are closely related in the real world, eg clustering the
Web pages of different universities. However, existing clustering approaches neglect the …