The support vector machine under test

By David Meyer, Friedrich Leisch, Kurt Hornik. Neurocomputing, 2003

815 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/cfe2f6989505a98e81943aaf3a70151ebad0d471

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In Analytical Chemistry

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

Multivariate Regression and Machine Learning with Sums of Separable Functions (2009, methodology): “For the first two Friedman data sets we achieve the best performance in comparison to the benchmark study [22], and on the third data set only a highly nonlinear approach achieves better results.”
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis (2013, neutral): “(4) Other supervised methods include Support Vector Machine (SVM), Bayesian classifier and rough set etc. Recently, there are a lot of papers showing the application of SVM to FDD [70], including diagnosis of the bars in the machine [32].”
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease (2012, background): “Some empirical studies have compared SVM and RF with no clear verdict about overall superiority of either approach [39], although it is well established that both classifiers perform well in general [22].”
kernlab - An S4 Package for Kernel Methods in R (2004, background): “Spectral clustering (Ng, Jordan, and Weiss 2001) is a recently emerged promising alternative to common clustering algorithms.”
Empirical characterization of random forest variable importance measures (2008, positive): “RF have been demonstrated to have excellent performance in comparison to other machine learning algorithms (Breiman, 2001b; Svetnik et al., 2003; Meyer et al., 2003) and traditional LR models (Peters et al., 2005).”
Model-Based Recursive Partitioning (2008, background): “…modern approaches to predictive modeling such as boosting (e.g., simple L 2 boosting by B¨uhlmann and Yu 2003), random forests (Breiman 2001) or support vector machines (Vapnik 1996) are often found to be superior to trees in purely predictive settings (e.g., Meyer, Leisch, and Hornik 2003).”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “For instance, support vector machines (discriminative approach) are widely used in cell biology (Meyer et al., 2003; Loo et al., 2007; Fuchs et al., 2010; Held et al., 2010; Neumann et al., 2010) owing to their good average performance among benchmark data sets (Meyer et al., 2003) and…”
Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, methodology): “Unba-lance of classification efficiency for small frequency vs. large frequency groups has been found in other real-data studies for Logistic Regression and Neural Networks [30,34,59,60].”
Data mining for credit card fraud: A comparative study (2011, methodology): “They are more convenient in application, with model selection built into the optimization procedure, and have also been found to outperform neural networks in classification problems [34].”