基于非线性共轭梯度法的最优增强算法

IF 0.3 Q4 MATHEMATICS, APPLIED

Journal of the Korean Society for Industrial and Applied Mathematics Pub Date : 2018-01-01 DOI:10.12941/JKSIAM.2018.22.001

Jooyeon Choi, Bora Jeong, Yesom Park, Jiwon Seo, Chohong Min

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引用次数: 1

摘要

摘要boost是监督学习中最成功的算法之一，它搜索最精确的弱分类器加权和。该搜索对应于具有非负性和仿射约束的凸规划。本文提出了一种新的共轭梯度算法，该算法具有改进的Polak-Ribiera-Polyak共轭方向。证明了该算法的收敛性，并将其成功应用于boosting。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AN OPTIMAL BOOSTING ALGORITHM BASED ON NONLINEAR CONJUGATE GRADIENT METHOD

ABSTRACT. Boosting, one of the most successful algorithms for supervised learning, searches the most accurate weighted sum of weak classifiers. The search corresponds to a convex programming with non-negativity and affine constraint. In this article, we propose a novel Conjugate Gradient algorithm with the Modified Polak-Ribiera-Polyak conjugate direction. The convergence of the algorithm is proved and we report its successful applications to boosting.

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来源期刊

Journal of the Korean Society for Industrial and Applied Mathematics MATHEMATICS, APPLIED-

自引率

33.30%

发文量