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 …

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 …

近年来, 迁移学习已经引起了广泛的关注和研究. 迁移学习是运用已存有的知识对不同但相关
领域问题进行求解的一种新的机器学习方法. 它放宽了传统机器学习中的两个基本假设:(1) …

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

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 …

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 …

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 …

近年来, 迁移学习已经引起了广泛的关注和研究. 迁移学习是运用已存有的知识对不同但相关
领域问题进行求解的一种新的机器学习方法. 它放宽了传统机器学习中的两个基本假设:(1) …

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 …