A Data Complexity Approach to Kernel Selection for Support Vector Machines
Abstract
We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels. Our resultsshow how the use of a Gaussian kernel produces a gram matrix with useful local information that has no equivalent counterpart inpolynomial kernels.By exploiting neighborhood information embedded by data complexity measures, we are able to carry out a form of meta-generalization.Our goal is to predict which data sets are more favorable to particular kernels (Gaussian or polynomial).The end result is a framework to improve the model selection process in Support Vector Machines.
Cite
Text
Valerio and Vilalta. "A Data Complexity Approach to Kernel Selection for Support Vector Machines." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9105Markdown
[Valerio and Vilalta. "A Data Complexity Approach to Kernel Selection for Support Vector Machines." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/valerio2014aaai-data/) doi:10.1609/AAAI.V28I1.9105BibTeX
@inproceedings{valerio2014aaai-data,
title = {{A Data Complexity Approach to Kernel Selection for Support Vector Machines}},
author = {Valerio, Roberto and Vilalta, Ricardo},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {3138-3139},
doi = {10.1609/AAAI.V28I1.9105},
url = {https://mlanthology.org/aaai/2014/valerio2014aaai-data/}
}