Support vector selection and adaptation and its application in remote sensing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Classification of nonlinearly separable data by nonlinear support vector machines is often a difficult task, especially due to the necessity of a choosing a convenient kernel type. Moreover, in order to get high classification accuracy with the nonlinear SVM, kernel parameters should be determined by using a cross validation algorithm before classification. However, this process is time consuming. In this study, we propose a new classification method that we name Support Vector Selection and Adaptation (SVSA). SVSA does not require any kernel selection and it is applicable to both linearly and nonlinearly separable data. The results show that the SVSA has promising performance that is competitive with the traditional linear and nonlinear SVM methods.

Original languageEnglish
Title of host publicationRAST 2009 - Proceedings of 4th International Conference on Recent Advances Space Technologies
Pages408-412
Number of pages5
DOIs
Publication statusPublished - 2009
Event4th International Conference on Recent Advances in Space Technologies 2009, RAST '09 - Istanbul, Turkey
Duration: 11 Jun 200913 Jun 2009

Publication series

NameRAST 2009 - Proceedings of 4th International Conference on Recent Advances Space Technologies

Conference

Conference4th International Conference on Recent Advances in Space Technologies 2009, RAST '09
Country/TerritoryTurkey
CityIstanbul
Period11/06/0913/06/09

Keywords

  • Classification of remote sensing data
  • Support vector machines
  • Support vector selection and adaptation

Fingerprint

Dive into the research topics of 'Support vector selection and adaptation and its application in remote sensing'. Together they form a unique fingerprint.

Cite this