Abstract: Minimum squared error algorithm is one of the classical pattern recognition and regression analysis methods.whose objective is to minimize the squared error summation between the output of linear function and the desired output.In this paper, the minimum squared error algorithm is modified by using kernel functions satisfying Mercer condition and regularization technique, and a nonlinear minimum squared error algorithm based on kernels and regularization technique, i.e., the regularized kernel form of minimum squared error algorithms is proposed. Its objective function includes squared error summation between the output of nonlinear function based on kernels and the desired output, and a proper regularization term. The regularization technique can handle ill-posed problems, reduce the solution space and control the generalization. Three regularization terms of square form are utilized in this paper. According to the probabilistic interpretation of regularization terms, the difference among three regularization terms is given in detail. The synthetic and real data are used to analyze the algorithm performance.