Description

Instance-based transfer learning methods try to reweight the samples in the source domain in an attempt to correct for marginal distribution differences. These reweighted instances are then directly used in the target domain for training.

Technical Details

Relationships

Related Techniques

• D3A-HBTL Hybrid-based Transfer Learning (Unpublished)
• D3A-SFTL Symmetric Feature-based Transfer Learning (Unpublished)
• D3A-HFBTL Heterogeneous Feature-based Transfer Learning (Unpublished)
• D3A-HTL Heterogeneous Transfer Learning (Unpublished)
• D3A-PBTL Parameter-based Transfer Learning (Unpublished)
• D3A-RBTL Relational-based Transfer Learning (Unpublished)
• D3A-AFTL Asymmetric Feature-based Transfer Learning (Unpublished)
• D3A-HAFBTL Heterogeneous Asymmetric Feature-based Transfer Learning (Unpublished)