Sentence examples for multivariate discretization that from inspiring English sources

Recent studies have focused on multivariate discretization that considers relations among attributes.

Prof. Dave Berners gave some good lectures on circuit discretization that will be helpful.

Biometric discretization in the literature can generally be divided into two broad categories: supervised and unsupervised discretization (discretization that makes use of class labels of the samples and discretization that does not, respectively).

Although several multivariate discretization algorithms overcome the drawback of univariate discretization methods by considering interactions among attributes, there are some weaknesses.

ICA [22], Cluster-Ent-MDLP [23], HDD [3], and Cluster-RS-Disc [24] are examples of multivariate discretization methods.

Despite the reported improvement, some multivariate discretization algorithms such as EMD [31] do not concentrate the natural group of data.

Babuska & Vogelius   [40] already observed for conforming discretizations, that the sequence of discrete approximations U (T ℓ ) always converges.

Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails.

This paper presents two novel, highly effective graph clustering-based discretization algorithms that are graph clustering-based discretization of splitting method (GraphS) and merging method (GraphM).

Here we consider two discretization schemes that are common in animal movement studies, namely spatial and temporal discretization (Chapter 7 in [31]).

The discretization algorithm that obtains the lowest rank value is set as a control algorithm.