Sentence examples for generally linear models from inspiring English sources

Generally, linear models represent each example E as a feature vector Φ E) ∈ ℜ D and then estimate a linear function f(E) = w TΦ E) whose sign is used to predict whether or not the example E is positive or negative.

PM2.5 species were modeled as continuous variables, linearity of outcome exposure associations was examined using various coding methods (e.g., categories based on percentiles) and found to be generally linear; models using categories of species exposure were monotonic increasing or decreasing (data not shown).

Table 2: General linear models reported in this article.

***P<0.001, multi-level generalized linear model.

*Adjusted general linear model.

Our results indicate that the non-linear RBF models had very similar prediction performance as the generally used linear model GBLUP.

Generally, a linear model represents each example E by a feature vector representation Φ(E) ∈ ℜ D and then estimates a linear function f(E) = w TΦ E) whose sign is used to predict whether the example E is classified into positive or negative.

Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks.

These linear models generally assume that the effect of a SNP in one population is the same in another population.

Therefore we generally prefer to use linear models unless we have strong evidence or prior knowledge that a model should be non-linear.

The linear models generally fitted the data well, but the deprivation gap is not simply the difference between survival in the richest and poorest groups: it is a fitted estimate of the difference, incorporating the data for all deprivation groups.