Research On Twin Support Vector Machine Classification And Regression Algorithm

Support Vector Machines is a research hotpot in the DataMining area,and Twin Support Vector Machines is a new continuationof the method of Support Vector Machines,it is a new Data Miningtechnology that was supported in 2007,it is rapidly developingin Theoretical research and practical application.Support Vector Machines is a learning theory based onstatistical machine learning methods ,it adopts Structural RiskMinimization.compared with some other methods of Data Mining,the obvious advantages are:It obtains Solid theoretical basis。It is based onOptimization theory,and it expected to be effective means toovercome Traditional difficult such as Dimension disasters；By constructing two Parallel hyperplanes,and using theBiggest interval principle to get a Convex quadratic optimizationproblem。We need less parameters to use Support Vector Machines,and it is easy to grasp for Actual workers.In 2007,Jayadeva and his parteners puted forward the new method of Twin Support Vector Machines in Two types ofclassification problem。Compared with the method of Support VectorMachines,the obvious advantages of Twin Support Vector Machinesare:It supports Tectonic 2-planes of Support Vector Machines toExtended to the Non-parallel situation,and got Convex quadraticprograms;Twin Support Vector Machines supports Vector machinesolution of the optimization problems to resolve into two Smalleroptimization problems,and solve easily,then need less time tocalculate.This context will introduce two types of classification andregression algorithm of Twin Support Vector Machines whichinclude Standard twin support vector classifier machine and itsimprovement,Twin Support Vector Regression,some Importantoptimization problem solving methods in Twin Support VectorMachines.