The analysis of stress errors to word targets (i.e., non-dominant stress words pronounced with the dominant stress, i.e. "regularisation" errors) showed that, in agreement with the predictions, these errors were significantly more likely after nonword primes (13.4%) than after word primes (3.6%), [t1 (23) = 5.02, p<.001; t2 (82) = 2.44, p<.05].
For the α value resulting in the minimum classification error the regularisation curves are shown in Additional file 2: Figure S2.
A direct approach is to optimise a cost function based on the margin error, which also incorporates regularisation terms for controlling capacity.
Here, μ > 0 is a regularisation parameter which balances the approximation error and sparseness of Φ and imposes a unique solution.
Typically, their errors have been classified as regularisation related to the assignment of word stress, i.e. those patients mostly produced the regular or dominant stress pattern avoiding the irregular or infrequent pattern while preserving syllable and phoneme structures [ 26- 33].
The penalty term, λ C T RC, where λ is a smoothing parameter that defines the degree of regularisation, is added to compensate for random error, and is based on the total curvature of the fitted curve, R = ∫ D 2 ϕ s D 2 ϕ T s ds, where D ϕ(s) is the second derivative of the vector of basis functions ϕ(t).
Conversely, the two versions of the Smagorinsky model are found unable to ensure regularisation while showing a strong sensitivity to numerical errors.
One of the most popular means of addressing premature declaration of convergence due to low error gradients in parameter identification is Tikhonov regularisation.
The characteristics of this method are: error functions for both the data and the regularisation terms are non-quadratic [14, 15]; the regularisation term is nonlinear isotropic [12, 16], based on the flow (flow-driven) and warping over several scales (multi-resolution strategy) is used in order to recover greater displacements with late linearisation [14, 17, 18] of the constancy terms.
Results show that a reasonable inference of recharge (average recharge error <10%) requires a surprisingly large number of preferred value regularisation constraints (>100 K values across the 129 km2 study area).
Regularisation is used to solve for the toroidal current densities, whereby the field error is minimised in conjunction with the total power of the coil.
