matrix regularization造句

Matrix Regularization has applications in matrix completion, multivariate regression, and multi-task learning.
where E defines the empirical error for a given W, and R ( W ) is a matrix regularization penalty.
In statistical learning point of view, the matrix completion problem is an application of matrix regularization which is a generalization of vector regularization.
The role of matrix regularization in this setting can be the same as in multivariate regression, but matrix norms can also be used to couple learning problems across tasks.
For example, matrix regularization with a Schatten 1-norm, also called the nuclear norm, can be used to enforce sparsity in the spectrum of a matrix.
It's difficult to find matrix regularization in a sentence. 用matrix regularization造句挺难的

Thus, by choosing a matrix regularization function as this type of norm, it is possible to find a solution that is sparse in terms of which kernels are used, but dense in the coefficient of each used kernel.