Support Vector Machines

Armin Shmilovici

doi:10.1007/978-3-031-24628-9_6

Support Vector Machines

Armin Shmilovici

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Support vector machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. An SVM classifier creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. In this chapter, we provide several formulations and discuss some key concepts.

Original language	American English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Pages	93-110
Number of pages	18
ISBN (Electronic)	9783031246289
DOIs	https://doi.org/10.1007/978-3-031-24628-9_6
State	Published - 1 Jan 2023

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

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10.1007/978-3-031-24628-9_6

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title = "Support Vector Machines",

abstract = "Support vector machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. An SVM classifier creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. In this chapter, we provide several formulations and discuss some key concepts.",

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Support Vector Machines

Abstract

All Science Journal Classification (ASJC) codes

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