Generalizing the Bias Term of Support Vector Machines
Abstract
Based on the study of a generalized form of representer theorem and a specific trick in constructing kernels, a generic learning model is proposed and applied to support vector machines. An algorithm is obtained which naturally generalizes the bias term of SVM. Unlike the solution of standard SVM which consists of a linear expansion of kernel functions and a bias term, the generalized algorithm maps predefined features onto a Hilbert space as well and takes them into special consideration by leaving part of the space unregularized when seeking a solution in the space. Empirical evaluations have confirmed the effectiveness from the generalization in classification tasks.
Cite
Text
Li et al. "Generalizing the Bias Term of Support Vector Machines." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Li et al. "Generalizing the Bias Term of Support Vector Machines." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/li2007ijcai-generalizing/)BibTeX
@inproceedings{li2007ijcai-generalizing,
title = {{Generalizing the Bias Term of Support Vector Machines}},
author = {Li, Wenye and Leung, Kwong-Sak and Lee, Kin-Hong},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {919-924},
url = {https://mlanthology.org/ijcai/2007/li2007ijcai-generalizing/}
}