Sentence examples for amount of regularisation from inspiring English sources

However, determination of the amount of regularisation is not straightforward in practical applications without prior knowledge of either the strength of the acoustic sources or the contaminating measurement noise.

Increasing the amount of regularisation increases the shrinkage of each coefficient.

A range of 13 Tikhonov regularisations was pre-computed to allow the right amount of regularisation to be selected according to the noise level found in the data.

However, large amounts of regularisation cause a loss in the spatial resolution of the MEG beamformer, hence regularisation should be used carefully, particularly if multiple sources in close proximity are expected.

In particular, because CHAOS-2s was built from data covering the 1997 2009.5 time period, with some amount of temporal regularisation, it may rightly be objected that its estimate of the 2004 field may be favourably biased towards the prediction inferred from SV1 and SV2, both based on CM4, which considered data up to 2002.5.

Nick Clegg's thought-through proposals on earned citizenship have put the difficult, but probably unavoidable, issue of regularisation of illegal immigrants into the political mainstream.

The Migrants' Rights Network report did a useful job in summarising the research on the issue of regularisation, debunking the myths and coming down firmly in its favour by outlining the humanitarian and economic case.

We also test the effect of changing the ratio of regularisation parameters.

Finally, through experimental demonstrations, some guidelines are proposed for determining the optimal degree of regularisation.

The regularisation parameter λ determines the amount of shrinkage of the coefficients with larger values inducing more shrinkage until no variables are contained in the model.

Finally, some methods aim to reduce the amount of parameter learning to avoid overfitting, achieved by regularisation techniques modifying the training objective function or limiting the parameter learning cycles (Duda et al, 2001).