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 hybridization of SVMs with meta‐heuristics are fully described in the first part of this paper. The adoption of SVMs in engineering is nowadays a fact. As we illustrate in this paper, SVMs can handle high‐dimensional, heterogeneous and scarcely labeled datasets very efficiently, and it can be also successfully tailored to particular applications. The second part of this review is devoted to different case studies in engineering problems, where the application of the SVM methodology has led to excellent results. First, we discuss the application of SVR algorithms in two renewable energy problems: the wind speed prediction from measurements in neighbor stations and the wind speed reconstruction using synoptic‐pressure data. The application of SVMs in noninvasive cardiac indices estimation is described next, and results obtained there are presented. The application of SVMs in problems of functional magnetic resonance imaging ( fMRI ) data processing is further discussed in the paper: brain decoding and mental disorder characterization. The following application deals with antenna array processing, namely SVMs for spatial nonlinear beamforming, and the SVM application in a problem of arrival angle detection. Finally, the application of SVMs to remote sensing image classification and target detection problems closes this review. WIREs Data Mining Knowl Discov 2014, 4:234–267. doi: 10.1002/widm.1125 This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning
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Support Vector Machines (SVMs) and Decision Trees (like Random Forests) are effective in simpler passenger counting scenarios where image features, like colors or texture are extracted first and then classified.
“Support Vector Machines (SVMs) [62] and Decision Trees (like Random Forests) [63] are effective in simpler passenger counting scenarios where image features, like colors or texture are extracted first and then classified.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [62]
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References 201 · Cited by 301
- Authors
- Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., & Camps‐Valls, G.
- Affiliations
- Universidad de Alcalá (ES)
- Universidad Rey Juan Carlos (ES)
- University of New Mexico (US)
- Universitat de València (ES)
- Corresponding author
- Camps‐Valls, G.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2014-04-28
- Volume
- 4
- Issue
- 3
- Pages
- 234-267
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W1770071703
- Cited by
- 301
- References
- 201
- Citations by year
- 2026: 27 · 2025: 66 · 2024: 41 · 2023: 42 · 2022: 30 · 2021: 16 · 2020: 24 · 2019: 10 · 2018: 16 · 2017: 12 · 2016: 7 · 2015: 8 · 2014: 2
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion)
- Funders
- Spanish Ministry of Science and Innovation (ECO2010-22065-C03-02, TEC2010-19263-TCM, and TIN2012-38102-C03-01)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- support vector machine, support vector regression, kernel methods, wind speed forecasting, functional MRI, brain decoding, antenna array beamforming, remote sensing image classification, target detection, metaheuristic hybridization, high-dimensional data
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