Support Vector Machines

A Shmilovici

doi:10.1007/978-0-387-09823-4_12

Support Vector Machines

A Shmilovici

Department of Software and Information Systems Engineering

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. A SVM classifiers 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. Here, we provide several formulations, and discuss some key concepts.

Original language	English
Title of host publication	Data Mining And Knowledge Discovery Handbook
Editors	O Maimon, L Rokach
Pages	231-247
Number of pages	17
Edition	2
ISBN (Electronic)	9780387098234
DOIs	https://doi.org/10.1007/978-0-387-09823-4_12
State	Published - 7 Jul 2010

Keywords

Hyperplane Classifiers
Kernel Methods
Margin Classifier
Support Vector Machines
Support Vector Regression

Access to Document

10.1007/978-0-387-09823-4_12

Cite this

@inbook{5ef6786a3ed44fbca4a629cd89443533,

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. A SVM classifiers 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. Here, we provide several formulations, and discuss some key concepts.",

keywords = "Hyperplane Classifiers, Kernel Methods, Margin Classifier, Support Vector Machines, Support Vector Regression",

author = "A Shmilovici",

year = "2010",

month = jul,

day = "7",

doi = "10.1007/978-0-387-09823-4_12",

language = "English",

isbn = "9780387098227",

pages = "231--247",

editor = "O Maimon and L Rokach",

booktitle = "Data Mining And Knowledge Discovery Handbook",

edition = "2",

}

TY - CHAP

T1 - Support Vector Machines

AU - Shmilovici, A

PY - 2010/7/7

Y1 - 2010/7/7

N2 - Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers 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. Here, we provide several formulations, and discuss some key concepts.

AB - Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers 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. Here, we provide several formulations, and discuss some key concepts.

KW - Hyperplane Classifiers

KW - Kernel Methods

KW - Margin Classifier

KW - Support Vector Machines

KW - Support Vector Regression

UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=bgu-pure&SrcAuth=WosAPI&KeyUT=WOS:000293102200012&DestLinkType=FullRecord&DestApp=WOS

U2 - 10.1007/978-0-387-09823-4_12

DO - 10.1007/978-0-387-09823-4_12

M3 - Chapter

SN - 9780387098227

SP - 231

EP - 247

BT - Data Mining And Knowledge Discovery Handbook

A2 - Maimon, O

A2 - Rokach, L

ER -

Support Vector Machines

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this