the-maximum-likelihood-estimate

代码说明：

  1、 极大似然估计 尝试用0~24阶多项式拟合，并用5折交叉验证选择最佳模型（多项式阶数及其系数，给出类似课件中的图），并画出最佳模型的拟合效果图（类似图1，蓝色点为训练样本、红色点为测试样本、绿色线为模型预测），给出该模型的测试误差。 2、 岭回归 多项式阶数为24，正则系数λ的取值范围为exp(-19)到exp(20)，采用并用5折交叉验证选择最佳模型。实验结果要求同1。 (1, the maximum likelihood estimate of 0 to 24 try-order polynomial fitting, with 5-fold cross-validation to select the best model (polynomial order and coefficients are given in Figure similar courseware), and draw the best model Fitting renderings (similar to Figure 1, a blue dot to the training sample, the test sample is red dot, the green line is the model prediction), the test error of the model are given. 2, ridge regression polynomial order of 24, the regular coefficient ranges λ is exp (-19) to exp (20), and with the use of 5-fold cross-validation to select the best model. The experimental results with a requirement.)

文件列表：

作业3代码源文件
...............\3.1test error.txt,208,2013-11-25


...............\3.2test error.txt,292,2013-11-25


...............\cv_mle.m,2383,2013-11-02
...............\cv_ridge.asv,3522,2013-11-02
...............\cv_ridge.m,3522,2013-11-02
...............\cv_risk.m,248,2013-10-29
...............\fdm.m,187,2013-10-27
...............\fdm_c.m,498,2013-11-02
...............\homework3_1.asv,1250,2013-11-25
...............\homework3_1.m,1256,2013-11-25
...............\homework3_2_c.asv,1417,2013-11-25
...............\homework3_2_c.m,1411,2013-11-25
...............\hw3data.mat,5184,2013-10-25
...............\lr_mle.m,161,2013-10-28
...............\qr_ols.m,1454,2013-10-27
...............\ridge_reg.m,284,2013-10-29
极大似然估计和岭回归报告.docx,42427,2013-12-10