Multi-source Transfer Learning Based on the Power Set Framework
Abstract Transfer learning is a great technology that can leverage knowledge from label-rich domains to address problems in similar domains that lack labeled data. Most previous works focus on single-source transfer, assuming the source domain contains sufficient labeled data and is close to the tar...
Main Authors: | Bingbing Song, Jianhan Pan, Qiaoli Qu, Zexin Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2023-06-01
|
Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s44196-023-00281-y |
Similar Items
-
Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy
by: Teng Cui, et al.
Published: (2022-09-01) -
Tri-Closed Sets In Tri-Topological Spaces
by: L Jeyasudha, et al.
Published: (2023-01-01) -
Neutrosophic Tri-Topological Space
by: Suman Das, et al.
Published: (2021-08-01) -
Uncertainty Multi-source Information Fusion for Intelligent Flood Risk Analysis Based on Random Set Theory
by: Yajuan Xie, et al.
Published: (2012-09-01) -
Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework
by: Leran Han, et al.
Published: (2020-11-01)