Support vector machines for classification and regression.

By Richard G. Brereton, Gavin Rhys Lloyd. In Analysis, 2010

2,601 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/62ed272e0e8b7be356c7f7595f5b7a22797a1c3e

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In Analytical Chemistry

In Biomedical Engineering

In Control and Systems Engineering

Citations in the corpus

Fault diagnosis of ball bearings using machine learning methods (2011, background): “The main difference between ANNs and SVMs is in the principle of risk minimization (RM) (Gunn, 1998).”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “vector regression [75,76], and, recently, variations on moving”
A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue (2011, methodology): “SVMs are a useful technique of data classification [220,221].”
Prediction of rainfall time series using modular soft computingmethods (2013, background): “33 (3) Support vector regression 34 SVR performs structural risk minimization (SRM) that aims at minimizing a bound 35 on the generalization error (Gunn, 1998; Kecman, 2001; Yu et al, 2006; Wu et al., 2008).”
Support vector machine in structural reliability analysis: A review (2023, positive): “The above-mentioned SVR algorithm can readily be implemented in the MATLAB platform with the Gunn toolbox [52] available at http://www.isis.ecs.soton.ac.uk/resources/svminfo/.”
Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs (2015, positive): “K ( xi, xj ) = exp (∥ xi − xj ∥ 2g2 ) (5) where g is the width of RBF (Gunn, 1998; Son, 2006).”
Predicting defect-prone software modules using support vector machines (2008, methodology): “Once the solution has been found, the decision can be constructed as: ∑ = + = n i i i i b k y f 1 ) , ( sign ) ( x x x λ (15) The following are the most common kernel functions in literature (Abe, 2005; Gun, 1998; Burges, 1998): 1.”
Enhanced pedotransfer functions with support vector machines to predict water retention of calcareous soil (2016, methodology): “Gunn (1998) used the term SVM to refer to both classification and regression methods, and the terms support vector classification (SVC) and support vector regression (SVR) to refer to the specific problems of classification and regression, respectively.”
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (2020, background): “From the late 1990s to the late 2000s, support vector machines maintained a high amount of research interest [80] and were considered state of the art.”
Polynomial-Chaos-based Kriging (2015, neutral): “…modeling (also called Kriging) Sacks et al. (1989); Rasmussen and Williams (2006); Santner et al. (2003); Stein (1999), and support vector machines Gunn (1998); Smola and Sch¨olkopf (2006); Vazquez and Walter (2003); Clarke et al. (2003), which have been extensively investigated in the last…”
Supervised classification and mathematical optimization (2013, background): “See also [18] [40] [108] [177] [113] for introductory surveys on SVMs.”