Machine Learning

, Volume 46, Issue 1, pp 71–89

A Probabilistic Framework for SVM Regression and Error Bar Estimation

  • J.B. Gao
  • S.R. Gunn
  • C.J. Harris
  • M. Brown
Article

DOI: 10.1023/A:1012494009640

Cite this article as:
Gao, J., Gunn, S., Harris, C. et al. Machine Learning (2002) 46: 71. doi:10.1023/A:1012494009640

Abstract

In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. This paper concentrates on the derivation of the evidence and error bar approximation for regression problems. An error bar formula is derived based on the ∈-insensitive loss function.

support vector machine (SVM) Gaussian process ∈-loss function error bar estimation 
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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • J.B. Gao
    • 1
  • S.R. Gunn
    • 1
  • C.J. Harris
    • 1
  • M. Brown
    • 2
  1. 1.Image, Speech and Intelligent System Research Group, Department of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  2. 2.Data Exploitation Group, D0170HS&T, IBM Hursley LaboratoryWinchesterUK

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