Robust classification and regression using support vector machines

2006 ◽  
Vol 173 (3) ◽  
pp. 893-909 ◽  
Author(s):  
Theodore B. Trafalis ◽  
Robin C. Gilbert
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Robust Classification ◽  
Vector Machines ◽  
Classification And Regression

Weighted proximal support vector machines: Robust classification

2005 ◽  
Vol 10 (3) ◽  
pp. 507-510
Author(s):  
Zhang Meng ◽  
Fu Li-hua ◽  
Wang Gao-feng ◽  
Hu Ji-cheng
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Robust Classification ◽  
Vector Machines

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 Incorporation of Support Vector Machines in the LIBS Toolbox for Sensitive and Robust Classification Amidst Unexpected Sample and System Variability

2012 ◽  
Vol 84 (6) ◽  
pp. 2686-2694 ◽  
Author(s):  
Narahara Chari Dingari ◽  
Ishan Barman ◽  
Ashwin Kumar Myakalwar ◽  
Surya P. Tewari ◽  
Manoj Kumar Gundawar
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Robust Classification ◽  
Vector Machines ◽  
System Variability

scholarly journals Robust Classification via Support Vector Machines

2021 ◽  
Author(s):  
Alexandru Vali Asimit ◽  
Ioannis Kyriakou ◽  
Simone Santoni ◽  
Salvatore Scognamiglio ◽  
Rui Zhu
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Robust Classification ◽  
Vector Machines

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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