31 citations · 91 across the 10 of their papers we have counts for
5 papers · 1 filter
End-To-End Graph-based Deep Semi-Supervised Learning
Zihao Wang, Enmei Tu, Zhou Meng
The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-b…
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection
Xiao Han, Zihao Wang, Enmei Tu +2
Deep learning demands a huge amount of well-labeled data to train the network parameters. How to use the least amount of labeled data to obtain the desired classification accuracy…
A Review of Semi Supervised Learning Theories and Recent Advances
Enmei Tu, Jie Yang
Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning.…
A Theoretical Study of The Relationship Between Whole An ELM Network and Its Subnetworks
Enmei Tu, Guanghao Zhang, Lily Rachmawati +2
A biological neural network is constituted by numerous subnetworks and modules with different functionalities. For an artificial neural network, the relationship between a network…
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
Enmei Tu, Yaqian Zhang, Lin Zhu +2
Nearest Neighbors (NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is a…