Support vector machines regression with l1-regularizer
- Resource Type
- Authors
- Tong, Hongzhi; Chen, Di-Rong; Yang, Fenghong
- Source
- Journal of Approximation Theory. (10):1331-1344
- Subject
- Computer Science::Machine Learning
Error decomposition
Support vector machines regression
Learning rate
Reproducing kernel Hilbert spaces
Coefficient regularization
- Language
- English
- ISSN
- 0021-9045
The classical support vector machines regression (SVMR) is known as a regularized learning algorithm in reproducing kernel Hilbert spaces (RKHS) with a ε-insensitive loss function and an RKHS norm regularizer. In this paper, we study a new SVMR algorithm where the regularization term is proportional to l1-norm of the coefficients in the kernel ensembles. We provide an error analysis of this algorithm, an explicit learning rate is then derived under some assumptions.