Support Vector Machines (SVMs) and Kernel methods have found a natural and effective coexistence since their introduction in the early 90s. In this article, we will describe the main concepts that motivate the importance of this relationship. In fact SVMs use kernels for learning linear predictors in high dimensional feature spaces. First, we will describe intuitively how this mechanism is realized, introducing the main concepts and definitions, i.e., maximum margin hyperplane, kernels and non-linearly separable problems. Then the main mathematical issues for linear and nonlinear SVM-based classification will be detailed. We will also introduce some important extensions of the SVMs ideas, by considering the Soft Margin Classification, SVM multi-class classification, SVM clustering, and SVM regression.

Zoppis, I., Mauri, G., Dondi, R. (2019). Kernel Methods: Support Vector Machines. In S. Ranganathan, M. Gribskov, K. Nakai, C. Schönbach (a cura di), Encyclopedia of Bioinformatics and Computational Biology : ABC of Bioinformatics. Vol.1: Methods (pp. 503-510). Cambridge : Elsevier [10.1016/B978-0-12-809633-8.20342-7].

Kernel Methods: Support Vector Machines

Zoppis, I;Mauri, G;Dondi, R
2019

Abstract

Support Vector Machines (SVMs) and Kernel methods have found a natural and effective coexistence since their introduction in the early 90s. In this article, we will describe the main concepts that motivate the importance of this relationship. In fact SVMs use kernels for learning linear predictors in high dimensional feature spaces. First, we will describe intuitively how this mechanism is realized, introducing the main concepts and definitions, i.e., maximum margin hyperplane, kernels and non-linearly separable problems. Then the main mathematical issues for linear and nonlinear SVM-based classification will be detailed. We will also introduce some important extensions of the SVMs ideas, by considering the Soft Margin Classification, SVM multi-class classification, SVM clustering, and SVM regression.
Capitolo o saggio
Classification; Clustering; Kernel methods; Maximun margin hyperplane; Regression; Soft margin; Support vector machine
English
Encyclopedia of Bioinformatics and Computational Biology : ABC of Bioinformatics. Vol.1: Methods
Ranganathan, S; Gribskov, M; Nakai, K; Schönbach, C (Editors in Chief)
2019
9780128114148
1
Elsevier
503
510
Zoppis, I., Mauri, G., Dondi, R. (2019). Kernel Methods: Support Vector Machines. In S. Ranganathan, M. Gribskov, K. Nakai, C. Schönbach (a cura di), Encyclopedia of Bioinformatics and Computational Biology : ABC of Bioinformatics. Vol.1: Methods (pp. 503-510). Cambridge : Elsevier [10.1016/B978-0-12-809633-8.20342-7].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/197333
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