Computational CyberPsychology deals with web users’ behaviors, and identifying their psychology characteristics using machine learning. Transfer learning intends to solve learning problems in target domain with different but related data distributions or features compared to the source domain, and usually the source domain has plenty of labeled data and the target domain doesn’t. In Computation...

Every year, billions of dollars are spent on development aid and training around the world. However, only 10% of this training results in the transfer of knowledge, skills, or behaviors learned in the training to the work place. Ideally, learning transfer produces effective and continued application by learners of the knowledge and skills they gained through their learning activities. Currently...

Neural networks (NNs) are often used as surrogates or emulators of partial differential equations (PDEs) that describe the dynamics complex systems. A virtually negligible computational cost such makes them an attractive tool for ensemble-based computation, which requires a large number repeated PDE solutions. Since latter also needed to generate sufficient data NN training, usefulness NN-based...

Mismatching problem between the source and target noisy corpora severely hinder the practical use of the machine-learningbased voice activity detection (VAD). In this paper, we try to address this problem in the transfer learning prospective. Transfer learning tries to find a common learning machine or a common feature subspace that is shared by both the source corpus and the target corpus. The...

hh tt tt Abstruct—This paper aims to accelerate processes of actor-critic method, which is one of major reinforcement learning algorithms, by a transfer learning. In general, reinforcement learning is used to solve optimization problems. Learning agents acquire a policy to accomplish the target task autonomously. To solve the problems, agents require long learning processes for trial and error....

The effects on transfer of learning multiple instantiations were investigated. Undergraduate college students learned one or more artificial instantiations of a simple mathematical concept. Some students were presented with instantiations that communicated concreteness relevant to the to-be-learned concept, while others learned generic instantiations involving abstract symbols. Learning one or ...

We survey various transfer methods in Q-learning, a type of reinforcement learning, and present a variation on fixed sub-transfer which we call dynamic sub-transfer. We describe the pros and cons of dynamic sub-transfer as compared with the other transfer methods, and we describe qualitatively the situations where this method would be preferred over the fixed version of sub-transfer.