A review of support vector machines with respect to spatial data

A review of support vector machines with respect to spatial data

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.

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Bibliographic Details
Main Authors: Kohram, Mojtaba, Md. Sap, Mohd. Noor
Format: Conference or Workshop Item
Published: 2007
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/25373/
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Institution: Universiti Teknologi Malaysia
id my.utm.25373
record_format eprints
spelling my.utm.253732017-08-06T03:20:35Z http://eprints.utm.my/id/eprint/25373/ A review of support vector machines with respect to spatial data Kohram, Mojtaba Md. Sap, Mohd. Noor QA75 Electronic computers. Computer science A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. 2007 Conference or Workshop Item PeerReviewed Kohram, Mojtaba and Md. Sap, Mohd. Noor (2007) A review of support vector machines with respect to spatial data. In: Postgraduate Annual Research Seminar (PARS’ 07), 2007, UTM, Johor Bahru.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Kohram, Mojtaba
Md. Sap, Mohd. Noor
A review of support vector machines with respect to spatial data
description A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
format Conference or Workshop Item
author Kohram, Mojtaba
Md. Sap, Mohd. Noor
author_facet Kohram, Mojtaba
Md. Sap, Mohd. Noor
author_sort Kohram, Mojtaba
title A review of support vector machines with respect to spatial data
title_short A review of support vector machines with respect to spatial data
title_full A review of support vector machines with respect to spatial data
title_fullStr A review of support vector machines with respect to spatial data
title_full_unstemmed A review of support vector machines with respect to spatial data
title_sort review of support vector machines with respect to spatial data
publishDate 2007
url http://eprints.utm.my/id/eprint/25373/
_version_ 1643647576279351296

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