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Mixed-transfer: Transfer learning over mixed graphs
Ben Tan
, Erheng Zhong
,
Michael K. Ng
, Qiang Yang
Department of Mathematics
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
15
Citations (Scopus)
Overview
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Dive into the research topics of 'Mixed-transfer: Transfer learning over mixed graphs'. Together they form a unique fingerprint.
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Keyphrases
Heterogeneous Transfer Learning
100%
Transfer Learning
100%
Mixed Graph
100%
Feature Space
66%
Occurrence Data
66%
Personal Bias
66%
Target Domain
33%
Label Propagation
33%
Knowledge Transfer
33%
Performance Improvement
33%
Learning Problems
33%
Source Domain
33%
Novel Algorithm
33%
Flickr
33%
Text-to-image
33%
Data Sparsity Problem
33%
Image Features
33%
Cross-domain
33%
Transition Probability
33%
Probability Graph
33%
Learning Strategies
33%
Four States
33%
Real-time Task
33%
New Learning
33%
Heterogeneous Data Sources
33%
Harmonic Functions
33%
Transfer Learning Algorithm
33%
Feature Mapping
33%
Building Model
33%
Random Walk Process
33%
Annotated Images
33%
Computer Science
Transfer Learning
100%
Feature Space
50%
Learning Algorithm
25%
Sparsity
25%
Learning Problem
25%
image feature
25%
Feature Mapping
25%
Random Walk Process
25%
Knowledge Transfer
25%