Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression

2004 ◽  
Vol 23 (5) ◽  
pp. 311-318 ◽  
Author(s):  
Holger Fröhlich ◽  
Jörg K. Wegner ◽  
Andreas Zell
Keyword(s):  
Support Vector Machines ◽  
Subset Selection ◽  
Support Vector ◽  
Vector Machines ◽  
Optimal Descriptor ◽  
Classification And Regression

Support Vector Machines for classification and regression

The Analyst ◽  
2010 ◽  
Vol 135 (2) ◽  
pp. 230-267 ◽  
Author(s):  
Richard G. Brereton ◽  
Gavin R. Lloyd
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Classification And Regression

scholarly journals KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN METODE SUPPORT VECTOR MACHINES (SVM) DAN CLASSIFICATION AND REGRESSION TREES (CART) MENGGUNAKAN GUI R (Studi Kasus di Kabupaten Wonosobo Tahun 2018)

2020 ◽  
Vol 9 (4) ◽  
pp. 525-534
Author(s):  
Lutfia Nuzula ◽  
Alan Prahutama ◽  
Arief Rachman Hakim
Keyword(s):  
Support Vector Machines ◽  
Classification Accuracy ◽  
Regression Trees ◽  
Poverty Line ◽  
Classification And Regression Trees ◽  
Support Vector ◽  
Poor People ◽  
Central Java ◽  
Vector Machines ◽  
Classification And Regression

The poor are people who have average monthly expenditures per capita below the poverty line. Wonosobo District became the poorest district in Central Java in 2011-2018, although the percentage of poor people has decreased every year. It cannot be separated from the efforts of the Wonosobo District Government to overcome poverty through various programs. This study classified households in Wonosobo District in 2018 as poor and non-poor based on influencing factors. This study used the Support Vector Machines (SVM) method to be compared with the Classification and Regression Trees (CART) method. It used the data from the 2018 National Socio-Economic Survey of Central Java with a total of 795 observations. Result of the research using the SVM method and the RBF kernel, the classification accuracy reaches 89.82% then the classification accuracy using the CART method reaches 87.08%. GUI designed by RShiny package can make easier for users to analyze the SVM and CART with the valid output. 

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