Sentence examples for regression solutions from inspiring English sources

The pooled values across the six permutations for C llr mean and 95% CI based on the different sparse representation regression solutions and scoring methods evaluated on the 60 female speaker database and 90 male speaker database under studio clean conditions are given in Figure 5 (top and bottom row, respectively).

It can be shown that, given h and λ, the RKHS regression solutions satisfy the linear system: [ 1 ′ R − 1 1 1 ′ R − 1 K h K ′ h R − 1 1 K ′ h R − 1 K h + 1 λ − 1 K h ] [ 1 ′ R − 1 y K ′ h R − 1 y ].

This means that in the least squares regression case, all the entries of the regression solution preserve the original speaker weightings, and all speakers in the background set in this case are used for typicality evaluation in the likelihood ratio calculation; and hence, there is no restriction in terms of test conditions for typicality in comparison with the ℓ 1 norm minimization case.

As an example, for the case of ℓ 1 norm minimization, the entries of the regression solution were forced to contain mostly zero entries, that is the technique forces the weights of the speakers from background set who are least similar to the test speaker to zero, and thus ignores the contribution of those speakers in the likelihood computation.

The response variable of gene i is denoted as y i, the design variables of TFs as X and the regression solution as.

When the number of variables is large or when variables are highly correlated, these techniques can offer a better regression solution than classical regression methods and other machine learning methods such as support vector machine (SVM) [ 15].

As a result, the least-squares residual is R 2 = ∑ i = 1 N ((x i − x c ) 2 + (y i − y c ) 2 − r ) 2. This equation is non-quadratic in the circle parameters (x c, y c, r); therefore no linear regression solution exists as written (although non-linear regression techniques can be applied).

The values û and v ^ that minimize (7) is the ordinary least squares regression solution when predicting t ˜ i from b ˜ i and g ˜ i. Moreover the estimate of αt i in each position i is obtained as the residual of the regression for that positions value, i.e. t ^ i = t ˜ i - n ^ i and the estimates are easily calculated using any software library that offers least squares regression modelling.

In this paper, the systematic and efficient development of multi-scale models, their interconnections, analysis, parameter regression and solution through the modelling framework is presented.

A major drawback of regression-based solutions of linear differential (difference) equations systems is the necessity of applying numerical derivatives of small sample size and noisy data, which have a strong influence on the resulting network and modelled dynamics.

In contrast to the previous approaches using particle filter, Gaussian approximation and regression-based solutions, our proposed approach, HMEnPF, can retain the first, second, third central and fourth central moments through filtering steps to estimate near optimal parameter values by the EM-algorithm.