Support Vector Classification with Input Data Uncertainty
Abstract
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the statistical reasoning, we propose a novel formulation of support vector classification, which allows uncer- tainty in input data. We derive an intuitive geometric interpretation of the proposed formulation, and develop algorithms to efficiently solve it. Empirical results are included to show that the newly formed method is superior to the standard SVM for problems with noisy input.
Cite
Text
Bi and Zhang. "Support Vector Classification with Input Data Uncertainty." Neural Information Processing Systems, 2004.Markdown
[Bi and Zhang. "Support Vector Classification with Input Data Uncertainty." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/bi2004neurips-support/)BibTeX
@inproceedings{bi2004neurips-support,
title = {{Support Vector Classification with Input Data Uncertainty}},
author = {Bi, Jinbo and Zhang, Tong},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {161-168},
url = {https://mlanthology.org/neurips/2004/bi2004neurips-support/}
}