Sentence examples for risk of regularization from inspiring English sources

A potential risk of regularization, as always with multi-objective optimization, is that for a low λ the regularization term has no effect, whereas for a large λ the parameter estimation algorithm might focus on minimizing the regularization term while describing the experimental data inaccurately.

The model includes two parts: log likelihood to control the fit of data to model (i.e., empirical risk) and regularization quantizer to control the generalization ability of model (i.e., general risk).

L1-regularization reduces the risk of over-fitting by biasing the feature weights towards zero, thereby only allowing associations with strong evidence to have non-zero weights [18].

For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization.

Different norms have different effects of regularization.

For different types of regularization, we establish energy estimates.

This point is illustrated using the Bayesian interpretation of regularization.

It was the first of a series of "regularization programs" that continued over the following decade.

Due to the dimensionality of the features (320 dimensions), some form of regularization was advisable.

Both L1-norm log linear regression and ridge regression solve this problem by means of regularization.

Typically, the amount of regularization needs to be optimized for a given classifier.