Sentence examples for regression transformation from inspiring English sources

The adaptation is performed using a linear regression transformation matrix based on an NMF framework.

This method requires only a small amount of parallel data, where a linear regression transformation matrix is used to adapt a source dictionary to a target dictionary and it is estimated in an NMF framework.

In VC, the source dictionary is constructed using sufficient source speaker data, and it is adapted using a small amount of parallel data (about ten words only) in order to obtain the target dictionary, where a linear regression transformation matrix (affine matrix) is trained based on NMF.

Raw data were first imported into a Genetraffic duo database (Iobion Informatics, Toronto, Canada), local background-subtracted and normalized using a Lowess (locally weighted linear regression) transformation.

The second design we assess for comparison is based on Bayesian isotonic regression transformation (BIT) [ 23].

As in the previous section, we use Bayesian isotonic regression transformation to compute and monitor toxicity continuously.

Values of a and k were derived from linear regressions of the logarithmic regression transformations: (2) Log   ⁡ Y = Log   ⁡ a + k · Log   ⁡ X + Log   ⁡ ε, where Y was the dependent variable (natural logarithms, i.e., log peak VO2) and body size (i.e., log body mass and log fat-free mass).

Given the development of a suitable regression-based transformation, TTU regression permits conversion of outcomes commonly used in clinical trials into the common metric of QALYs.

Training is performed in this article with constrained structural maximum a posteriori linear regression (CSMAPLR) transformation [32, 33].

Adaptive training is performed with constrained maximum likelihood linear regression (CMLLR) transformation [33] of our previously trained average neutral walk HSMM model.

However, to prove hypotheses of the regression analysis, transformation to the hs-CRP variable was applied to satisfy the normal distribution condition of the parameter.