Extreme Learning Machines Based on Least Absolute Deviation and Their Applications in Analysis Hard Rate of Licorice Seeds
Liming Yang, Junjian Bai and Qun Sun

doi:10.3970/cmes.2015.108.049
Source CMES: Computer Modeling in Engineering & Sciences, Vol. 108, No. 1, pp. 49-65, 2015
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Keywords Extreme learning machine, robust regression, least absolute deviation estimation, near-infrared spectroscopy.
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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