The Comparative Research On Support Vector Regression Machines Based On Different Loss Functions

Machine learning is mainly used to analyze and process data,and to mine the potential relevant information of the data.In the era of big data,it's a hot topic.to accurately and quickly mine the relations of the information.The Support Vector Machines is a new technology for data mining proposed by Vapnik.,mainly used in pattern recognition,regression analysis and other aspects.The sparsity of algorithm is the advantage of SVM.and the result of the algorithm is only affected by a part of samples,so SVM has strong anti-interference ability.In addition,the SVM algorithm can also prevent over-fitting by adding regularizationThe main researches content of this paper:(1)what the connection is between the ?-Support Vector Regression algorithm(?-SVR)which is derived from the maximization margin method and the Structural Risk Minimization Regression algorithm(t-SVR)in statistics.(2)By improving the loss function of the v-Support Vector Regression machine(v-SVR),this paper proposes Gauss Loss Support Vector Regression algorithm(GSVR)and Mixed Loss Support Vector Regression algorithm(MSVR),and a comparative research among the ?-SVR?v-SVR?LSSVR?GSVR and MSVR with specific dataThe main conclusions of this paper:(1)If the optimal solution of the ?-SVR algorithm is(w*(C),b*,?(*)when the parameter C is determined and the parameter t of the t-SVR algorithm satisfies t=?=w*(C)?,then we can prove that these two algorithms are equivalent.(2)Both linear regression and nonlinear regression problems,when the parameter v of GSVR and MSVR approaches positive infinity,GSVR and MSVR are equivalent to LSSVR with the same C.In addition,according to the data experimental results,the regression results of training set get by ?-SVR?LSSVR?GSVR and MSVR algorithms are better than v-SVR algorithm.(3)For the nonlinear regression problems,combined with the two practical problems in this paper,the prediction ability of GSVR algorithm is better than others.