期刊
NEUROCOMPUTING
卷 74, 期 16, 页码 2541-2551出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2010.12.041
关键词
Face recognition; Discriminative locality alignment; Support vector machine; Extreme learning machine; One-against-all; One-against-one
Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets. (C) 2011 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据