CWT-Support Vector Regression Model and Its Application

Abstract:

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Near-infrared spectroscopy (NIR) analytical technique is simple, fast and low cost, making neither pollution nor damage to the samples, and can determine many components simultaneously. Continuous wavelet transform (CWT), as an application direction of the wavelet analysis, is keener to the signal slight change. Support vector machine (SVM) is based on the principle of structural risk minimization, which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization. In this paper, we use CWT- SVM model to predict meat’s component. Compared with Partial Least Squares (PLS) and SVR, we get more satisfactory result.

Info:

Periodical:

Advanced Materials Research (Volumes 113-116)

Edited by:

Zhenyu Du and X.B Sun

Pages:

207-210

DOI:

10.4028/www.scientific.net/AMR.113-116.207

Citation:

J. F. Liu et al., "CWT-Support Vector Regression Model and Its Application", Advanced Materials Research, Vols. 113-116, pp. 207-210, 2010

Online since:

June 2010

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Price:

$35.00

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