Sentence examples for prediction losses from inspiring English sources

The min quality tolerates the loss of all p-pictures and some prediction losses and audio outages.

Notwithstanding, GLM is known to suffer prediction losses if one has heavy tailed data (kurtosis) even after log retransformation of the dependent variable.

The learning objective of the proposed method consists of a combination of three parts: the kernel matching criterion, prediction losses, and graph Laplacian regularization.

GLM prediction losses (relative to the log-OLS estimator) increases with the coefficient of kurtosis of the log scale error or when the true underlying model is a log normal with constant error variance (on the log scale).

Prediction losses are incorporated into the learning process as to minimize training loss on both the labeled source and mapped labeled target instances for a prediction model trained on the source data.

The universal prediction algorithm is expected to perform at least as well as the best member of set (mathbb {M}) in terms of prediction loss [19, 29, 30].

Prediction loss function, regret, and redundancy are discussed in Subsection 3.3, while Subsection 3.4 provides an overview of Bayesian-based techniques.

Theoretical and numerical results demonstrate that the relaxed Lasso produces sparser models with equal or lower prediction loss than the regular Lasso estimator for high-dimensional data.

Performance comparison metrics such as secondary user's throughput, spectrum interference and wastage, and probability of error (or mean square error) are generally defined based on the probability density of the one step-ahead prediction, as well as, the prediction loss function.

In agreement with this prediction, loss of variation is a frequent, although not ubiquitous, feature of introductions [5].

To verify this prediction, loss-of-function experiments were performed by injecting Xag and Xagr2 MO.