Sentence examples for by minimizing the prediction from inspiring English sources

We use a forward linear predictor, which as usual is obtained by minimizing the prediction error in the least squares sense, cf. [23] for details.

where the parameters μ(k) are obtained by a simple non iterative computation involving the LP coefficients[25] by minimizing the prediction error variance R e, as μ ( k ) = 1 P e ∑ i = 0 M − k ( M + 1 − k − 2 i ) a i a i + k ∗ k = 0 … M μ ∗ ( − k ) k = − M. … − 1 (35).

This means that the likely cause of an observed action is inferred by minimizing the prediction error at all levels of the hierarchy involved during action-observation.

The inference is made by minimizing the prediction error at extant nodes as well as an additional L2-norm regularization factor.

The standardized difference, and thereby the number of relevant genes, is chosen by minimizing the prediction error using 10-fold balanced, leave-10%-out cross-validation within the training set.

By minimizing the prediction error at all the levels of action representation, the most likely cause of the action, at both the intention and the intermediate goal level, will be inferred.

In the proposed method, we elaborately design the down-sampling structures and interpolation schemes for each directional intra prediction mode by minimizing the spatial prediction distance.

As a result, we propose to optimize the prediction filter p j ( H H ) by minimizing the global prediction error, as described in detail in the next section.

For training of the l PSSM W j, a regularized least-squares classifier [see e.g. (Hoff et al., 2008)] was built to discriminate between positive and negative word match examples by minimizing the following prediction error: (4) E (W ) = ∑ i ∈ I train (y i − ∑ j = 1 l tr (W j T X i j ) ) 2 + λ ∑ j tr (W j T W j ) where tr indicates the trace operator and W = [ W 1,  …  , W l ].

The parameters α i 's and b are trained from the training dataset by maximizing the separation margin and minimizing the prediction error on training data.

This dynamic system of feedback/feedforward recurrent loops aims at minimizing the prediction error.