1、 format compact; x = (-1:0.1:1); y = -x.2; model = svmtrain(y,x,-s 3 -t 2 -c 2.2 -g 2.8 -p 0.01); . WARNING: using -h 0 may be faster optimization finished, #iter = 104 nu = 0.224906 obj = -0.675055, rho = 0.698514 nSV = 9, nBSV = 2 py , mse = svmpredict( y , x , model ); Mean squared error = 9.5276
2、8e-005 (regression) Squared correlation coefficient = 0.999184 (regression) figure; plot(x , y, o); hold on; plot(x, py, r*); legend(原始数据 ,回归数据 ); grid on; testx = 1.1;1.2;1.3; display(真实数据 ) 真实数据 testy = -testx.2 testy = -1.2100 -1.4400 -1.6900 ptesty , tmse = svmpredict( testy , testx , model); Mean squared error = 0.0976693 (regression) Squared correlation coefficient = 0.914542 (regression) display(预测数据 ); 预测数据 ptesty ptesty = -1.1087 -1.1913 -1.2200
