Despite the growing growth of technology, handwritten signature has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten signature verification and recognition based on the Shearlet transform and transfer learning is proposed. Since, a large percentage of handwritten signatures are composed of curves and the performance of a sig...

With transfer learning, one set of tasks is used to bias learning and improve performance on another task. However, transfer learning may actually hinder performance if the tasks are too dissimilar. As described in this paper, one challenge for transfer learning research is to develop approaches that detect and avoid negative transfer using very little data from the target task.

Weobserve standard transfer learning can improve prediction accuracies of target tasks at the cost of lowering their prediction fairness – a phenomenon we named discriminatory transfer. We examine prediction fairness of a standard hypothesis transfer algorithm and a standardmulti-task learning algorithm, and show they both su er discriminatory transfer on the real-world Communities and Crime da...

This study was conducted to compare the impact of two vocabulary learning techniques, namely context learning and translation learning, on vocabulary recall of sixty pre-university Iranian learners of English as a foreign language. They were divided into two groups of high and low proficient. In regard to two vocabulary learning conditions, each group was divided into two subgroups of fifteen. ...

Intermanual transfer, i.e., generalization of motor learning across hands, is a well-accepted phenomenon of motor learning. Yet, there are open questions regarding the characteristics of this transfer, particularly the intermanual transfer of dynamic learning. In this study, we investigated intermanual transfer in a force field adaptation task concerning the direction and the coordinate frame o...

This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning sig...

Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem. Various transfer learning methods are designed ...

Transfer of learning is using previous knowledge in novel contexts. While this is a basic assumption of the educational process, students may not always perceive all the options for using what they have learned in different, novel situations. Within the framework of transfer of learning, this study outlines an attitudinal survey concerning faculty and student attitudes about transfer of learnin...

Heterogeneous transfer learning has been proposed as a new learning strategy to improve performance in a target domain by leveraging data from other heterogeneous source domains where feature spaces can be different across different domains. In order to connect two different spaces, one common technique is to bridge feature spaces by using some co-occurrence data. For example, annotated images ...