In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore solutions to transfer learning within reinforcement learning through the use of spectral methods. Proto-value functions (PVFs) are basis functions computed from a spectral analysis of random walks on the state space graph. They naturally lead to the ability to transfer knowledge and representati...

Through this project, we researched on transfer learning methods and their applications on real world problems. By implementing and modifying various methods in transfer learning for our problem, we obtained an insight in the advantages and disadvantages of these methods, as well as experiences in developing neural network models for knowledge transfer. Due to time constraint, we only applied a...

Machine learning processes consist in collecting data, obtaining a model and applying it to a given task. Given a new task, the standard approach is to restart the learning process and obtain a new model. However, previous learning experience can be exploited to assist the new learning process. The two most studied approaches for this are metalearning and transfer learning. Metalearning can be ...

We develop two related themes, learning procedures and knowledge transfer. This paper introduces two methods for learning procedures and one for transferring previously-learned knowledge to a slightly different task. We demonstrate by experiment that transfer accelerates learning.

This study examined learning and transfer of a simple mathematical concept when learning a symbolic sentential format versus learning a diagrammatic format. Undergraduate college students learned an instantiation of a cyclic group and were then given a test of a novel isomorphic group of the same order followed by a test of a novel non-isomorphic group of a higher order. The results were that b...

one of the indicators for evaluating the effectiveness of farmers’ training is the transfer of learning by them. the aim of current study was to investigate factors influencing the implementation of the recommendations of research finding diffusion-push plans by farmers. research population consisted of farmers who participated at least in one of the research finding diffusion-push plans in far...

Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinforcement learning show that transfer may significantly improve the learning performance, most of the literature on transfer is focused on batch learning tasks. In this paper we study the problem of sequential transfer...

Education aims to equip learners with knowledge about principles that can be applied across a wide range of situations but often people do not recognize structural similarity between known cases and novel problems. Several approaches for designing learning materials to foster transfer of principles have been proposed including learning a generic instantiation, comparing instantiating concrete c...

Transfer learning aims to solve new learning problems by extracting and making use of the common knowledge found in related domains. A key element of transfer learning is to identify structured knowledge to enable the knowledge transfer. Structured knowledge comes in different forms, depending on the nature of the learning problem and characteristics of the domains. In this article, we describe...