View source for Support Vector Machine (SVM) Classification System

A [[Support Vector Machine (SVM) Classification System]] is a [[SVM System]] that is a [[Supervised Classification System]].
* <B>AKA:</B> [[Support Vector Machine (SVM) Classification System|Support Vector Classification System]].
* <B>Example(s):</B> 
** [[SVMlight]].
** [[TinySVM]].
** [[LIBSVM System]].
** A [[sklearn.svm]] Classification System such as:
*** [[sklearn.svm.LinearSVC]].
*** [[sklearn.svm.NuSVC]].
*** [[sklearn.svm.SVC]].
** …
* <B>Counter-Example(s):</B> 
** an [[SVM Ranking System]].
** an [[SVM Regression System]].
* <B>See:</B> [[Neural Network System]].

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== References ==

=== 2018 ===
* (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Support_vector_machine Retrieved:2018-4-8.
** In [[machine learning]], '''support vector machines''' ('''SVMs''', also '''support vector networks'''<ref name="CorinnaCortes"> Cortes, Corinna; Vapnik, Vladimir N. ([[1995]]). “Support-vector networks". Machine Learning. 20 (3): 273–297. [https://doi.org/10.1007%2FBF00994018 doi:10.1007/BF00994018]. </ref>) are [[supervised learning model]]s with associated learning [[algorithm]]s that analyze data used for [[Statistical classification|classification]] and [[regression analysis]]. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-[[probabilistic classification|probabilistic]] [[binary classifier|binary]] [[linear classifier]] (although methods such as [[Platt scaling]] exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.         <P>        In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the [[kernel trick]], implicitly mapping their inputs into high-dimensional feature spaces.         <P>        When data are not labeled, supervised learning is not possible, and an [[unsupervised learning]] approach is required, which attempts to find natural [[Data clustering|clustering of the data]] to groups, and then map new data to these formed groups. The '''support vector clustering'''<ref name="HavaSiegelmann">Ben-Hur, Asa; Horn, David; Siegelmann, Hava; and Vapnik, Vladimir N.; "Support vector clustering"; (2001); ''Journal of Machine Learning Research'', 2: 125–137 </ref> algorithm created by [[Hava Siegelmann]] and [[Vladimir Vapnik]], applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications.

=== 2017 ===
* (Zhang, 2017) ⇒ Zhang X. (2017) [https://link.springer.com/referenceworkentry/10.1007/978-1-4899-7687-1_810 Support Vector Machines]. In: [[Sammut, C.]], [[Webb, G.I.]] (eds) [https://link.springer.com/referencework/10.1007/978-1-4899-7687-1 Encyclopedia of Machine Learning and Data Mining]. Springer, Boston, MA
** QUOTE: [[Support vector machines (SVMs)]] are a [[class]] of [[linear algorithm]]s which can be used for [[classification]], [[regression]], [[density estimation]], [[novelty detection]], etc. In the simplest case of [[two-class classification]], [[SVM]]s find a [[hyperplane]] that separates the two classes of [[data]] with as wide a margin as possible. This leads to good [[generalization]] accuracy on unseen data and supports specialized [[optimization method]]s that allow [[SVM]] to learn from a [[Big Data|large amount of data]].

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[[Category:Concept]]
[[Category:Machine Learning]]