Journal Of Big Data· 2016Q1
A survey of transfer learning
- 6,199citations
- Q1SCImago
- 2016year
Short summary
This survey formally defines transfer learning, a methodology enabling high-performance learners trained with easily obtained data from different domains when training data is expensive or difficult to collect.
AI-generated from the title and abstract; the full text is not read.
Key points
- Defines transfer learning as a method to build high-performance learners when training data is scarce or difficult to obtain.
- Reviews current solutions and applications of transfer learning.
- Highlights that transfer learning methods are independent of data size and suitable for big data.
- Provides information on software downloads for transfer learning solutions.
AI-generated from the title and abstract; the full text is not read.
Abstract
Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.
The authors' abstract, as published at the source. Journal Of Big Data, 2016 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science