Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets

2017 ◽  
Vol 28 (1) ◽  
pp. 94-106 ◽  
Author(s):  
Shengyu Nan ◽  
Lei Sun ◽  
Badong Chen ◽  
Zhiping Lin ◽  
Kar-Ann Toh
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets ◽  
Density Dependent

Nonlinear clustering-based support vector machine for large data sets

2008 ◽  
Vol 23 (4) ◽  
pp. 533-549 ◽  
Author(s):  
Yongqiao Wang ◽  
Xun Zhang ◽  
Souyang Wang ◽  
K.K. Lai
Keyword(s):  
Support Vector Machine ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets

Large data sets classification using convex–concave hull and support vector machine

2012 ◽  
Vol 17 (5) ◽  
pp. 793-804 ◽  
Author(s):  
Asdrúbal López Chau ◽  
Xiaoou Li ◽  
Wen Yu
Keyword(s):  
Support Vector Machine ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets

scholarly journals Randomized kernel methods for least-squares support vector machines

2017 ◽  
Vol 28 (02) ◽  
pp. 1750015 ◽  
Author(s):  
M. Andrecut
Keyword(s):  
Least Squares ◽  
Kernel Method ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Classification Problems ◽  
Vector Machines ◽  
Multi Class Classification

The least-squares support vector machine (LS-SVM) is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the LS-SVM classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.

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