OP-ELM: Optimally Pruned Extreme Learning Machine
OP-ELM: Optimally Pruned Extreme Learning Machine
复制标题
DOI：
10.1109/tnn.2009.2036259
复制
发表时间：
2010-01-01
影响因子：
--
通讯作者：
Lendasse, Amaury
中科院分区：
文献类型：
--
作者：
Miche, Yoan;Sorjamaa, Antti;Lendasse, Amaury
本文介绍了最优剪枝极限学习机（OP-ELM）方法。它基于原始的极限学习机（ELM）算法，并增加了额外的步骤，使其更具鲁棒性和通用性。详细介绍了整个方法，然后将其应用于几个回归和分类问题。计算时间和精度（均方误差）的结果与原始ELM和其他三种广泛使用的方法进行了比较：多层感知器（MLP），支持向量机（SVM）和高斯过程（GP）。正如回归和分类的实验所表明的那样，除了原始ELM之外，所提出的OP-ELM方法的执行速度比本文中使用的其他算法快几个数量级。尽管简单且性能快速，但OP-ELM仍然能够保持与SVM性能相当的精度。OP-ELM的工具箱在网上公开提供。
In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems. Results for both computational time and accuracy (mean square error) are compared to the original ELM and to three other widely used methodologies: multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate, the proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM. Despite the simplicity and fast performance, the OP-ELM is still able to maintain an accuracy that is comparable to the performance of the SVM. A toolbox for the OP-ELM is publicly available online.