Description

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.

Technical Details

Relationships

Related Techniques

• D3A-IBTL Instance-based Transfer Learning (Unpublished)
• D3A-HBTL Hybrid-based Transfer Learning (Unpublished)
• D3A-SFTL Symmetric Feature-based Transfer Learning (Unpublished)
• D3A-HFBTL Heterogeneous Feature-based Transfer Learning (Unpublished)
• D3A-HTL Heterogeneous Transfer Learning (Unpublished)
• D3A-PBTL Parameter-based Transfer Learning (Unpublished)
• D3A-AFTL Asymmetric Feature-based Transfer Learning (Unpublished)
• D3A-HAFBTL Heterogeneous Asymmetric Feature-based Transfer Learning (Unpublished)