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 Support Vector Machines Study on English Isolated-Word-Error Classification and Regression

2013 ◽  
Vol 5 (2) ◽  
pp. 531-537
Author(s):  
Abu Bakar Hasan ◽  
Tiong Sieh Kiong ◽  
Johnny Koh Siaw Paw ◽  
Ahmad Kamal Zulkifle
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Classification And Regression ◽  
Isolated Word ◽  
Error Classification

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. 

scholarly journals A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Hilario Gómez-Moreno ◽  
Pedro Gil-Jiménez ◽  
Sergio Lafuente-Arroyo ◽  
Roberto López-Sastre ◽  
Saturnino Maldonado-Bascón
Keyword(s):  
Support Vector Machines ◽  
Noise Reduction ◽  
Digital Images ◽  
State Of The Art ◽  
Noise Removal ◽  
Support Vector ◽  
Salt And Pepper Noise ◽  
Vector Machines ◽  
Classification And Regression ◽  
Salt And Pepper

We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.

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