Machine Learning

, Volume 61, Issue 1, pp 151–165

A Fast Dual Algorithm for Kernel Logistic Regression

Authors

    • Yahoo! Research Labs
  • K. B. Duan
    • Control Division, Department of Mechanical EngineeringNational University of Singapore
  • S. K. Shevade
    • Department of Computer Science and AutomationIndian Institute of Science
  • A. N. Poo
    • Control Division, Department of Mechanical EngineeringNational University of Singapore
Article

DOI: 10.1007/s10994-005-0768-5

Cite this article as:
Keerthi, S.S., Duan, K.B., Shevade, S.K. et al. Mach Learn (2005) 61: 151. doi:10.1007/s10994-005-0768-5

Abstract

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Keywords

classification logistic regression kernel methods SMO algorithm

Copyright information

© Springer Science + Business Media, Inc. 2005