Sentence examples for a regularization factor from inspiring English sources

We achieve this by regularizing the L2-norm of the model parameters using a regularization factor.

To solve this problem, we will multiply S by a regularization factor.

It removes requirement of any arbitrary/user-based decision for choosing a regularization factor thus minimizes the chance for biasedness or errors.

The parameter (delta in mathbb {R}_) is a regularization factor, generally adopted as a small constant, used to avoid divisions by 0. Let us consider the problem of identifying the unknown system (mathbf {w}_{o}in mathbb {R}^{N+1}), such that begin{array}{*{20}l} d(k) = mathbf{w}_{o}^{T} mathbf{x}(k) + n k), end{array} (6).

Similar to the stage index p, the weighting factor ω also acts as a regularization factor where the performance of the LPIC detector is expected to improve for small values of ω, but after a certain threshold the noise enhancement effect starts taking place and the performance of the LPIC detector worsens.

where H K denotes the value of H at the center of element K,∥·∥ F is the Frobenius matrix norm, and α h is a regularization factor that is defined by alpha_{h} = frac{1}{| Omega |} left(sum_{Kin mathcal{T}_{h}} |K| cdot | H_{K} |_{F} right).

We added a fluxes-measurement regularization factor λ in order to generate an initial point closer to the final estimation (and thus speed up the convergence process).

We use a L2-norm regularization factor to restrict the search space of model parameters to avoid overfitting.

We do so by using a L2-norm regularization factor, which is determined by 5-fold cross-validation, to restrict the search space of model parameters.

The inference is made by minimizing the prediction error at extant nodes as well as an additional L2-norm regularization factor.

The meta-parameters of the LS-SVM model with a Gaussian kernel function are γ (the width of the Gaussian kernels) and C (regularization factor).