2014
DOI: 10.1002/widm.1125
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Support vector machines in engineering: an overview

Abstract: This paper provides an overview of the support vector machine (SVM) methodology and its applicability to real‐world engineering problems. Specifically, the aim of this study is to review the current state of the SVM technique, and to show some of its latest successful results in real‐world problems present in different engineering fields. The paper starts by reviewing the main basic concepts of SVMs and kernel methods. Kernel theory, SVMs, support vector regression (SVR), and SVM in signal processing and hybr… Show more

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Cited by 159 publications
(83 citation statements)
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“…Support vector machine (SVM) 28 is a supervised learning model that is based on statistical learning theory, the concept of decision planes and structural risk minimization. The SVM algorithm constructs a hyperplane in a high-dimensional space for the prediction of class labels.…”
Section: Support Vector Machine In Textile Industrymentioning
confidence: 99%
“…Support vector machine (SVM) 28 is a supervised learning model that is based on statistical learning theory, the concept of decision planes and structural risk minimization. The SVM algorithm constructs a hyperplane in a high-dimensional space for the prediction of class labels.…”
Section: Support Vector Machine In Textile Industrymentioning
confidence: 99%
“…Although there are several versions of the SVR, the classical model, -SVR, described in detail in [76], is the one considered in this work because it has been shown to be very useful in a large variety of problems in science and engineering [77].…”
Section: Support Vector Regressionmentioning
confidence: 99%
“…SVR algorithms are adequate for a large variety of regression problems, since they do not only take into account the error estimates of the data, but also the generalization of the regression model (the capability of the model to improve the prediction when a new dataset is evaluated). Although there are several versions of SVR, the e-SVR classical model described in detail by Smola and Scholkopf (2004), which has been used in a large number of applications in science and engineering (Salcedo et al, 2014), is considered in this work. The SVR method for regression uses a given a set of training vectors = {(x i , o i ), i = 1,...l}, where x i stands for the inputs, and o i stands for the TCO variable to be predicted.…”
Section: Support Vector Regression Algorithmsmentioning
confidence: 99%
“…In this paper we propose a novel system for TCO prediction in a daily time-horizon (24 h) that combines a powerful regression methodology (support vector regression, SVR) (Salcedo et al, 2014) with different predictive variables coming from satellite data (Suomi National Polar-orbiting Partnership [NPP] satellite), numerical models (Global Forecasting System [GFS] model) and in-situ measurements. To our knowledge, there are not previous works dealing with the SVR methodology in TCO prediction.…”
Section: Introductionmentioning
confidence: 99%