Papers
1
Total Citations
18
H-Index
1
About
Yebo Li is a researcher whose work lies at the intersection of machine learning, optimization, and computational mathematics. His most-cited paper, "Householder transformation based sparse least squares support vector regression" (2015, 18 citations), introduces a novel approach to improving the efficiency and sparsity of least squares support vector regression—a key tool in pattern recognition and data analysis. By leveraging Householder transformations, Li’s method reduces computational complexity while maintaining predictive accuracy, offering a practical solution for large-scale datasets. This contribution reflects his broader focus on developing robust, sparse algorithms for regression and classification tasks. With 18 citations, this work has garnered attention from peers seeking efficient kernel-based learning techniques. Li’s research is particularly valuable for students and researchers in machine learning and applied mathematics, as it bridges theoretical rigor with real-world applicability. His achievements underscore a commitment to advancing computational methods that balance performance with interpretability, making his work a touchstone for those exploring sparse modeling and support vector machines.
Research Focus
Key Achievements
Top Papers
- 1