A modified Lanczos Algorithm for fast regularization of extreme learning machines

被引：13

作者：

Hu, Renjie ^{[1
]}

Ratner, Edward ^{[1
]}

Stewart, David ^{[2
]}

Bjork, Kaj-Mikael ^{[3
,4
]}

Lendasse, Amaury ^{[1
]}

机构：

[1] Univ Houston, ILT Dept, Houston, TX 77004 USA

[2] Univ Iowa, Dept Math, Iowa City, IA 52242 USA

[3] Arcada Univ Appl Sci, Helsinki, Finland

[4] Hanken Sch Econ, Helsinki, Finland

来源：

NEUROCOMPUTING | 2020年 / 414卷

关键词：

Extreme Learning machines; Lanczos Algorithm; Regularization; Neural Networks; Regression; Classification; REGRESSION; SYSTEMS; EQUATIONS; ELM;

D O I：

10.1016/j.neucom.2020.07.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new regularization for Extreme Learning Machines (ELMs). ELMs are Randomized Neural Networks (RNNs) that are known for their fast training speed and good accuracy. Nevertheless the complexity of ELMs has to be selected, and regularization has to be performed in order to avoid under-fitting or overfitting. Therefore, a novel Regularization is proposed using a modified Lanczos Algorithm: Iterative Lanczos Extreme Learning Machine (Lan-ELM). As summarized in the experimental Section, the computational time is on average divided by 4 and the Normalized MSE is on average reduced by 11%. In addition, the proposed method can be intuitively parallelized, which makes it a very valuable tool to analyze huge data sets in real-time. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：172 / 181

页数：10

共 41 条

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