The purpose of this study, in an environment of Internet project-based learning, is to undertake research on the effects of thinking styles on learning transfer. In this study, we establish an environment that incorporates project-based learning and Internet. Within this environment, we divide our sample of elementary school students into four groups: Executive Group, Legislative Group, Judicia...

This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Qlearning and Sarsa with ...

Reinforcement learning methods have successfully been applied to build autonomous agents that solve many sequential decision making problems. However, agents need a long time to learn a suitable policy, specially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning (TL) framework to accelerate learning by exploiting two knowledge sources: (i)...

A crucial issue in machine learning is how to learn appropriate representations for data. Recently, much work has been devoted to kernel learning, that is, the problem of finding a good kernel matrix for a given task. This can be done in a semi-supervised learning setting by using a large set of unlabeled data and a (typically small) set of i.i.d. labeled data. Another, even more challenging pr...

Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many realworld scenarios, this assumption may not hold. As a result, the constructed feat...

This paper encapsulates the use reinforcement learning on raw images provided by a simulation to produce a partially trained network. Before training is continued, this partially trained network is fed different raw images that are more tightly coupled with a richer representation of the non-simulated environment. The use of transfer learning allows for the model to adjust to this richer repres...

Specificity has always been considered one of the hallmarks of perceptual learning, suggesting that performance improvement would reflect changes at early stages of visual analyses (e.g., V1). More recently, however, this view has been challenged by studies documenting complete transfer of learning among different spatial locations or stimulus orientations when a double-training procedure is ad...

Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information to tasks for which no data have been observed so far. In this work we study ...

Transfer learning is commonly used to address the problem of the prohibitive need in annotated data when one want to classify visual content with a Convolutional Neural Network (CNN). We address the problem of the universality of the CNN-based representation of images in such a context. The state-of-the-art consists in diversifying the source problem on which the CNN is learned. It reduces the ...