Support Vector Machines in R

By Alexandros Karatzoglou, David Meyer, Kurt Hornik. 2006

673 citations recorded by the source corpus.

Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1c0020ad0f0a6c7bed9fc395b292d9007e17d72f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, methodology): “Since not all classes are linearly separable, SVM is optimized to search for a non-linear hyperplane in a multidimensional feature space using a kernel function (Karatzoglou, Meyer, and Hornik 2006).”
Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy (2020, background): “Advantages of SVM models are their ability to handle high-dimensional multivariate spaces (Karatzoglou et al., 2006) and to deal with noisy patterns and multi-modal class distributions of soil properties (Gholizadeh et al.”
Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables (2011, methodology): “SVM is more rapid and tends to distinguish optimally between groups and predicts entities while minimizing the loss of information (Guo et al. 2004; Karatzoglou et al. 2006).”
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information (2014, background): “Support Vector Machines Support Vector Machines (SVM), formally described by Vapnik (1998), has the ability to define non-linear decision boundaries in high-dimensional variable space by solving a quadratic optimization problem (Hsu et al., 2010; Karatzoglou et al., 2006).”
Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal (2014, methodology): “The SVM model was created by an R package e1071 [37] with RBF as a kernel K(xi, xj ) = e−r |xi −xj |, where r = 0.”
A deep learning approach for detecting traffic accidents from social media data (2018, methodology): “This section compares the classification results of the DBN with LSTM as well as a supervised learning method: Artificial Neural Network (ANN), Support Vector Machine (SVMs) (Karatzoglou et al., 2005) and a topic modeling method: supervised Latent Dirichlet allocation (sLDA) (Mochihashi, 2009).”
Automatic deception detection in Italian court cases (2013, methodology): “Linear kernel functions are usually considered useful in text categorization, where often one deals with large sparse data vectors, as in the study of Karatzoglou et al. (2006). Nevertheless in the following experiments radial kernel functions are employed, because on DECOUR they gave more uniform r”
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, background): “…is the penalty of a misclassified observation that controls how far on the wrong side of the hyperplane a point can lie when the training data cannot be classified without error, that is when the objects are not linearly separable and a soft separating non-linear margin is required [41,42].”
Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery (2020, background): “A number of studies have found that the RBF is superior to the polynomial kernel for the classification of remote sensing data([40-42]).”