Physics

Transfer of learning

Sample (material)

Domain adaptation

Scope (computer science)

Mathematics

Domain (mathematical analysis)

Situational ethics

Optics

Machine learning

Mathematical analysis

Adaptation (eye)

Political science

Artificial intelligence

Chemistry

Focus (optics)

Programming language

Computer science

Chromatography

Selection (genetic algorithm)

Classifier (UML)

Journal Of Big Data

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

Transfer learning: a friendly introduction