Robust kernel-based regression

Budi Santosa*, Theodore B. Trafalis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this research, a robust optimization approach applied to support vector regression (SVR) is investigated. A novel kernel based-method is developed to address the problem of data uncertainty where each data point is inside a sphere. The model is called robust SVR. Computational results show that the resulting robust SVR model is better than traditional SVR in terms of robustness and generalization error.

Original languageEnglish
Pages (from-to)424-429
Number of pages6
JournalWSEAS Transactions on Systems
Issue number2
Publication statusPublished - Feb 2006


  • Kernel Method
  • Regression
  • Robust Optimization
  • Support Vector Machine
  • Uncertainty


Dive into the research topics of 'Robust kernel-based regression'. Together they form a unique fingerprint.

Cite this