Feature Kernel Functions: Improving SVMs Using High-Level Knowledge
Abstract
Kernel functions are often cited as a mechanism to encode prior knowledge of a learning task. But it can be difficult to capture prior knowledge effectively. For example, we know that image pixels of a handwritten character result from a few strokes from a single writing implement; it is not clear how to express this in a kernel function. We investigate an explanation based learning (EBL) paradigm to generate specialized kernel functions. These embody novel high-level features that are automatically constructed from the interaction of prior knowledge and training examples. Our empirical results showed that the performance of the resulting SVM surpasses that of a conventional SVM on the challenging task of classifying handwritten Chinese characters.
Cite
Text
Sun and DeJong. "Feature Kernel Functions: Improving SVMs Using High-Level Knowledge." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.157Markdown
[Sun and DeJong. "Feature Kernel Functions: Improving SVMs Using High-Level Knowledge." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/sun2005cvpr-feature/) doi:10.1109/CVPR.2005.157BibTeX
@inproceedings{sun2005cvpr-feature,
title = {{Feature Kernel Functions: Improving SVMs Using High-Level Knowledge}},
author = {Sun, Qiang and DeJong, Gerald},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {177-183},
doi = {10.1109/CVPR.2005.157},
url = {https://mlanthology.org/cvpr/2005/sun2005cvpr-feature/}
}