Search Results for author:
Found 3 papers, 0 papers with code
Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem
The Classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields.
The classification for High-dimension low-sample size data
In this paper, we propose a novel classification criterion on HDLSS, tolerance similarity, which emphasizes the maximization of within-class variance on the premise of class separability.
Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems
Various applications in different fields, such as gene expression analysis or computer vision, suffer from data sets with high-dimensional low-sample-size (HDLSS), which has posed significant challenges for standard statistical and modern machine learning methods.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
