Back to Search
Start Over
Kernel-Free Quadratic Surface Regression for Multi-Class Classification.
- Source :
- Entropy; Jul2023, Vol. 25 Issue 7, p1103, 18p
- Publication Year :
- 2023
-
Abstract
- For multi-class classification problems, a new kernel-free nonlinear classifier is presented, called the hard quadratic surface least squares regression (HQSLSR). It combines the benefits of the least squares loss function and quadratic kernel-free trick. The optimization problem of HQSLSR is convex and unconstrained, making it easy to solve. Further, to improve the generalization ability of HQSLSR, a softened version (SQSLSR) is proposed by introducing an ε -dragging technique, which can enlarge the between-class distance. The optimization problem of SQSLSR is solved by designing an alteration iteration algorithm. The convergence, interpretability and computational complexity of our methods are addressed in a theoretical analysis. The visualization results on five artificial datasets demonstrate that the obtained regression function in each category has geometric diversity and the advantage of the ε -dragging technique. Furthermore, experimental results on benchmark datasets show that our methods perform comparably to some state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]
- Subjects :
- LEAST squares
COMPUTATIONAL complexity
CLASSIFICATION
DATA visualization
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 25
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 168601330
- Full Text :
- https://doi.org/10.3390/e25071103