Sentence examples for regularization introduced from inspiring English sources

have been shown to eventually encode purely local information when the number of data points grows, we show that the regularization introduced by the use of cluster cores solves this issue.

Regularization introduces additional information to a function in order to solve an ill-posed problem or avoid overfitting.

Regularization introduces additional information in an inverse problem in order to solve an ill-posed problem or to prevent over-fitting.

Regularization introduces a prior that imposes constraints in the STRF estimate and, under noisy conditions, the specific regularization used by the model can introduce biases in the estimates [4].

In the presence of limited or noisy data (a common scenario in neurophysiological experiments), regularization introduces a prior that constrains the STRF estimate in a way that is independent of the underlying tuning properties of the neuron, but can introduce additional biases in the STRF.

The Tikhonov regularization is introduced to manage the ill-conditioned least squares system; in particular, the performances of various regularization matrices are investigated.

A specific formulation of the proposed method, called hybrid regularization, is introduced and investigated.

Meanwhile, in order to derive a sparse ensemble, l1 regularization is introduced to control the size of ensembles.

Then a projection matrix with L2,1-norm regularization is introduced into the model for subspace learning and jointly sparse feature extraction and selection.

In general, regularization is introduced to prevent over fitting when the number of observations is not sufficiently larger than that of unknown parameters.

The regularization programme introduced by the United States in 1986 targeted an entire migrant group, that is, all irregular migrants from all nationalities (case 1).