A Fast Dual Algorithm for Kernel Logistic Regression

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 experim...

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Bibliographic Details
Published in:Machine learning 2005-11, Vol.61 (1-3), p.151-165
Main Authors: Keerthi, S S, Duan, K B, Shevade, S K, Poo, A N
Format: Article
Language:English
Subjects:
Optimization algorithms
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Summary: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.[PUBLICATION ABSTRACT]
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-005-0768-5