Sentence examples for of variable dependence from inspiring English sources

To investigate general characteristics of MixDTrees and compare it with other methods, we use simulated data from mixture models with different degrees of variable dependence, and apply several unsupervised learning methods.

Data We generate data from mixtures with four types of variable dependence ranging from: Gaussians with diagonal covariance matrix (Σ diag ), DTree with low variate dependence (Σ DTree −), DTree with high variate dependence (Σ DTree +) and Gaussians with full covariance matrix (Σ full ).

Different types of materials, however, showed variable dependence on particle size, and could be assigned to one of three distinct groups: (1) Samples with low surface area dependence on particle size and surface areas approximately two orders of magnitude higher than the surface area of smooth spheres of equivalent size.

For data processing, a probability theoretical approach is used based on involving the notion of consistency of measures of dependence of random variables.

In contrast to the canonical problems of classification or regression, in learning to rank we are required to learn some mapping from inputs to inter-dependent output variables, so that we may wish to model both stochastic orderings of variable states and statistical dependence relationships between variables.

Even if the building of a universal model is far from being completed due to the large number of variables and their dependence, this comparison categorizes previous models in a unique way, which made it easy for readers to use them in different applications.

Meanwhile, as the function HS k,n,r) is the function of three variables, its dependence is certainly also a variable k, respectively, n.

MI Mutual Information of two random variables is a measure of the variables' mutual dependence.

To understand and quantify the spatial dependence of variables, cross-variograms were calculated.

Here, we consider a constrained MOLP problem with multiple utility functions for each goal, and then assume the effects of independence and dependence of variables on the utility functions.

D-PNN is a new type of neural network, which function approximation and dependence of variables identification is based on a generalization of data relations.