Firstly,screen the descriptors using support vector machine regression(SVR) by leave-one-out method based on the minimum mean square error(MSE),get the optimal kernel and the corresponding retained descriptors.

For the generalized linear model with aggregated data:Y=Xβ+u,Eu=0,Var(u)=σ2∑,this paper is built two kinds of biased estimators: ridge estimator β(k)and improved ridge estimator β(k)which are discussed some superiority to the estimators in the sense of mean square error.

As to seemingly unrelated regression system,a new biased contracting estimator of the parameters is put forward,which is the combination of generalized ridge covariance-improved estimator and Stein estimator,and the good features of this estimator in mean square error is discussed.

A Motion Estimation Algorithm Based on the Quantized DCT Coefficients MSE Criterion;

For a class of seemingly unrelated regression system consisting of two equations,an improved estimation of principal components is proposed and the optimal properties are discussed in the sense of mean squared error(MSE).

Under the MSE criterion,some properties of the improved c-k estimators are given.

Computer simulations show that the proposed algorithms lead to faster convergence rates and lower steady-state Mean Square Error(MSE) after convergence compared with the concurrent CMA and SDD algorithm.

A fast encoding algorithm based on the Mean Square Error(MSE) distortion for vector quantization is introduced.

A fast encoding algorithm based on the mean square error(MSE) distance for vector quantization is introduced.

When the receiver detects the maximum frequency of Doppler-spread, the frequency of BEM is modified by a factor of 1/K, and improved mean-square error of channel can be obtained.