Sentence examples for by adding a regularization from inspiring English sources

Desired properties of the dictionary can be obtained by adding a regularization term such as the element-wise L1 matrix norm as imposed by the LASSO algorithm [41].

We extend the existing Laplacian SVM and present (S^3VM-R), by adding a regularization term to exploit exogenous information embedded in our feature space in favor of the task at hand.

In this paper we present a new framework based on the existing Laplacian SVM [14], by adding a regularization term to the standard optimization problem and solving the new optimization equation derived from there.

By adding a regularization term to improve the generalization performance and make the solution more robust, the resulting solution β is given by [18, 19] boldsymbol{beta} ={left({mathbf{H}}^{mathrm{T}}mathbf{H}+frac{mathbf{I}}{mathbf{C}}right)}^{-1}{mathbf{H}}^{mathrm{T}}mathbf{ Y (9).

To circumvent this problem, regularized CCA (rCCA) has been recently proposed by [ 1] when dealing with ill-conditioned covariance matrices by adding a regularization term on their diagonal.

rCCA solves the instability of the loadings due to multicollinearity by adding a regularization term on the diagonal of the ill-conditionned matrices, i.e. the covariance matrices.

Actually, the above-mentioned two techniques belong to the class of regularization-based techniques that can make trade-offs between directivity and robustness by adding a proper regularization parameter to all the diagonal elements of the noise covariance matrix.

To solve the ill-posed problem caused by noise, some algorithms improve the performance by adding a priori regularization term in the objective function [15 19].

We improved mm-tSNE by adding a Laplacian regularization term and subsequently provide an algorithm for optimizing the new objective function.

We improved mm-tSNE by adding a Laplacian regularization term to the cost function C (Y ) : (6) C Y = K L (P | | Q ) = (1 - λ ) ∑ i ∑ j ≠ i p i j l o g p i j q i j + λ π T L π where L = (diag (∑ i p i j ) - P ij ).

In order to avoid overfitting and instead of adding a regularization parameter in the criterion, early stopping is introduced as a regularization method for HK learning, which becomes HKES (Ho Kashyap with early stopping).