A Fast Dual Algorithm for Kernel Logistic Regression

被引：0

作者：

S. S. Keerthi

K. B. Duan

S. K. Shevade

A. N. Poo

机构：

[1] Yahoo! Research Labs,Control Division, Department of Mechanical Engineering

[2] National University of Singapore,Department of Computer Science and Automation

[3] Indian Institute of Science,Control Division, Department of Mechanical Engineering

[4] National University of Singapore,undefined

来源：

Machine Learning | 2005年 / 61卷

关键词：

classification; logistic regression; kernel methods; SMO algorithm;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：151 / 165

页数：14

共 6 条

[1]

Keerthi S. S.(2001)Improvements to Platt's SMO algorithm for SVM classifier design Neural Computation 13 637-649

[2]

Shevade S. K.(1998)Bayesian classification with Gaussian processes IEEE Transactions on PAMI 20 1342-1351

[3]

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[4]

Murthy K. R. K.(undefined)undefined undefined undefined undefined-undefined

[5]

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[6]

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