Traditional learning techniques have the assumption that training and test data are drawn from the same data distribution, and thus they are not suitable for dealing with the situation where new unlabeled data are obtained from fast evolving, related but different information sources. This leads to the crossdomain learning problem which targets on adapting the knowledge learned from one or more...

Transfer learning is one way to close the gap between the apparent speed of human learning and the relatively slow pace of learning by machines. Transfer is doubly beneficial in reinforcement learning where the agent not only needs to generalize from sparse experience, but also needs to efficiently explore. In this paper, we show that the hierarchical Bayesian framework can be readily adapted t...

Abbreviated Title 8 Intermanual transfer characteristics of dynamic learning 9 Abstract 19 Intermanual transfer, i.e., generalization of motor learning across hands, is a well-accepted phenomenon of 20 motor learning. Yet, there are open questions regarding the characteristics of this transfer, particularly, 21 intermanual transfer of dynamic learning. In this study, we investigated intermanual...

csMTL, or context-sensitive Multiple Task Learning, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for learning multiple tasks. The csMTL approach is demonstrated to produce hypotheses that are equivalent to or better than standard MTL hypotheses when learning a primary task in the presence of related and unrelated tasks....

We propose a framework for learning new target tasks by leveraging existing heterogeneous knowledge sources. Unlike the traditional transfer learning, we do not require explicit relations between source and target tasks, and instead let the learner actively mine transferable knowledge from a source dataset. To this end, we develop (1) a transfer learning method for source datasets with heteroge...

Learning the relational structure of a domain is a fundamental problem in statistical relational learning. The deep transfer algorithm of Davis and Domingos attempts to improve structure learning in Markov logic networks by harnessing the power of transfer learning, using the second-order structural regularities of a source domain to bias the structure search process in a target domain. We prop...

Transfer learning algorithms typically assume that the training data and the test data come from different distribution. It is better at adapting to learn new tasks and concepts more quickly and accurately by exploiting previously gained knowledge. Deep Transfer Learning (DTL) emerged as a new paradigm in transfer learning in which a deep model offer greater flexibility in extracting high-level...

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Crosscompany defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness. key words: machine learning,...

Test-enhanced learning and transfer for triple-associate word stimuli was assessed in three experiments. In each experiment, training and final-test trials involved the presentation of two words per triple associate (triplet), with the third word having to be retrieved. In agreement with the prior literature on different stimuli, training through testing with feedback yielded markedly better fi...

Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the target domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applications. The general idea of the existing methods is to utilize the common latent structure shared across domains as the bridge for knowledge transfer. These methods us...