A regularization method for the minimum estimation error

A new cost function of regularization for generalization is proposed. This cost function is derived from the maximum likelihood method using a modified sample distribution, and consists of a sum of square errors and a stabilizer which is an integrated square derivative. The regularization parameters which give the minimum estimation error can be obtained nonempirically. Numerical simulation shows that this cost function predicts the true error accurately and is effective in neural network learning.