Keerthi, SS and Duan, KB and Shevade, SK and Poo, AN (2005) A fast dual algorithm for kernel logistic regression. In: Machine Learning, 61 (1-3). pp. 151-165.
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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.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Springer.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||09 Jun 2010 05:47|
|Last Modified:||19 Sep 2010 06:00|
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