Performance Comparison for Four TSVR-type Learning Algorithms
LI Yan-meng
FAN Li-ya
Abstract：It is w ell know n that the computational complexity and sparsity of learning algorithms based on support vector regression machines (SVRs) are two main factors for analyzing and treating big data ,especially for high dimensional data .According to the two factors ,scholars did a lot of research work and proposed many improved SVR‐type learning algorithms .Among these improved algorithms , some have the basically same starting point ,just solving methods are slightly different ;some have dis‐tinctly different starting point and then result in different optimization problems ,but the solving meth‐ods are similar .For deep understanding these improved algorithms and being more selective in the appli‐cations ,this paper is devoted to analyze and compare the performance for four more representative TS‐VR‐type algorithms .
Keywords：twin support vector regression machineleast squaresboundaryparameter insensitive
Publication Date：2016-01-01
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：7( 1-7 )