Support vector machines in engineering: an overview

Salcedo‐Sanz, Sancho; Rojo-Álvarez, José Luis; Martínez‐Ramón, Manel; Camps‐Valls, Gustau

doi:10.1002/widm.1125

Cited by 159 publications

(83 citation statements)

References 175 publications

(199 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

Data mining and machine learning in textile industry

Yıldırım

Birant

Alpyıldız

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

show abstract

Section: Support Vector Machine In Textile Industrymentioning

confidence: 99%

Data mining and machine learning in textile industry

Yıldırım

Birant

Alpyıldız

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…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%

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

et al. 2017

View full text Add to dashboard Cite

Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML) regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines) and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains). Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

show abstract

“…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%

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

Carro-Calvo¹,

Casanova-Mateo²,

Sanz-Justo³

et al. 2017

Atm

View full text Add to dashboard Cite

RESUMENSe propone un nuevo método de pronóstico para la columna total de ozono (CTO) basado en la combinación de algoritmos de vectores de soporte para regresión (VSR) y variables de predicción provenientes del saté-lite de colaboración nacional en órbita polar Suomi, así como de modelos numéricos del Sistema Global de Predicción (SGP) y mediciones directas. Los datos de satélite incluyen perfiles de temperatura y humedad a diferentes alturas, y mediciones de CTO realizadas en los días anteriores al pronóstico. El modelo SGP proporciona datos de temperatura y humedad para el día del pronóstico. El sistema también considera los datos alternos de mediciones in situ, p. ej. de la profundidad óptica de aerosoles a diferentes longitudes de onda. Mediante la metodología VSR se puede obtener un pronóstico exacto de la CTO a partir de estas variables de predicción, con mejores resultados que los obtenidos con otros métodos de regresión, p. ej. redes neuronales. También se efectúa un análisis del mejor subconjunto de características del pronóstico de CTO. La parte experimental de la investigación consiste en la aplicación de VSR a datos de observación directa obtenidos en el laboratorio radiométrico de Madrid, España, donde están disponibles mediciones de ozono adquiridas por medio de un espectrofotómetro Brewer, lo que posibilita el entrenamiento del sistema y la evaluación de sus resultados. ABSTRACTThis paper proposes a novel prediction method for Total Column Ozone (TCO), based on the combination of Support Vector Regression (SVR) algorithms and different predictive variables coming from satellite data (Suomi National Polar-orbiting Partnership satellite), numerical models (Global Forecasting System model, GFS) and direct measurements. Data from satellite consists of temperature and humidity profiles at different heights, and TCO measurements the days before the prediction. GFS model provides predictions of temperature and humidity for the day of prediction. Alternative data measured in situ, such as aerosol optical depth at different wavelengths, are also considered in the system. The SVR methodology is able to obtain an accurate TCO prediction from these predictive variables, outperforming other regression methodologies such as neural networks. Analysis on the best subset of features in TCO prediction is also carried out in this paper. The experimental part of the paper consists in the application of the SVR to real data collected at the radiometric observatory of Madrid, Spain, where ozone measurements obtained with a Brewer spectrophotometer are available, and allow the system's training and the evaluation of its performance.

show abstract

Support vector machines in engineering: an overview

Cited by 159 publications

References 175 publications

Data mining and machine learning in textile industry

Data mining and machine learning in textile industry

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

Contact Info

Product

Resources

About