Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10994-005-0768-5
Title: A fast dual algorithm for kernel logistic regression
Authors: Keerthi, S.S.
Duan, K.B.
Shevade, S.K.
Poo, A.N. 
Keywords: Classification
Kernel methods
Logistic regression
SMO algorithm
Issue Date: Nov-2005
Citation: Keerthi, S.S., Duan, K.B., Shevade, S.K., Poo, A.N. (2005-11). A fast dual algorithm for kernel logistic regression. Machine Learning 61 (1-3) : 151-165. ScholarBank@NUS Repository. https://doi.org/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.
Source Title: Machine Learning
URI: http://scholarbank.nus.edu.sg/handle/10635/54130
ISSN: 08856125
DOI: 10.1007/s10994-005-0768-5
Appears in Collections:Staff Publications

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