Open Access

ARTICLE

Extreme Learning Machines Based on Least Absolute Deviation and Their Applications in Analysis Hard Rate of Licorice Seeds

Liming Yang^1,2, Junjian Bai¹, Qun Sun³

1 College of Science, China Agricultural University, Beijing, 100083, China.
2 Corresponding author: Liming Yang; E-mail: cauyanglm@163.com
3 College of Agriculture and Biotechnology, China Agricultural University, Beijing, 100193, China

Computer Modeling in Engineering & Sciences 2015, 108(1), 49-65. https://doi.org/10.3970/cmes.2015.108.049

Abstract

Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l₂-norm and l₁-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on eight different spectral regions show the feasibility and effectiveness of the proposed models.

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