Relational-based Transfer Learning

Relational-based Transfer Learning is a subfield of machine learning where knowledge and patterns learned from one domain, characterized by relational and structured data, are transferred to enhance the learning of another related domain. This approach leverages shared concepts, relations, and structures across domains, taking advantage of the rich semantic knowledge within relational data to improve learning performance in the target task.

References

V7 Labs. (n.d.). Transfer Learning Guide. Link.