A survey of large-scale graph-based semi-supervised classification algorithms
Semi-supervised learning is an effective method to study how to use both labeled data and unlabeled data to improve the performance of the classifier, which has become the hot field of machine learning in recent years. Graph-based semi-supervised learning is very promising among these Semi-supervise...
Main Authors: | Yunsheng Song, Jing Zhang, Chao Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co., Ltd.
2022-06-01
|
Series: | International Journal of Cognitive Computing in Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307422000201 |
Similar Items
-
Semi-Supervised Classification Based on Mixture Graph
by: Lei Feng, et al.
Published: (2015-11-01) -
Semi-Supervised Classification Based on Low Rank Representation
by: Xuan Hou, et al.
Published: (2016-07-01) -
GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification
by: Jianhe Li, et al.
Published: (2023-02-01) -
Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification
by: Guanghua Zhang, et al.
Published: (2021-01-01) -
Adaptive Semi-Supervised Classification by Joint Global and Local Graph
by: Siyang Deng, et al.
Published: (2019-01-01)