A fast dual algorithm for kernel logistic regression


Autoria(s): Keerthi, SS; Duan, KB; Shevade, SK; Poo, AN
Data(s)

01/11/2005

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/27180/1/a_fast.pdf

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.

Publicador

Springer

Relação

http://www.springerlink.com/content/w863504l47656216/

http://eprints.iisc.ernet.in/27180/

Palavras-Chave #Computer Science & Automation (Formerly, School of Automation)
Tipo

Journal Article

PeerReviewed