Examples of using Kernel method in English and their translations into Portuguese
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How kernel methods(such as convolution) work and their impact on computer vision.
For that purpose, tra! c models based on the kernel method and the mmfm with adjusted autocorrelation function are proposed.
Kernel method, nearest neighbor hierarchical technique, and spatial scan statistic.
Among the employed types of analyses, the highlights are correlations and geoprocessing techniques,especially through the Kernel method.
Machine learning, kernel methods, kernel independent component analysis and graphical models.
Spatial analysis and modeling tools were used to describe the existing patterns of the viral load density, using the Kernel method.
Research on machine learning theory, kernel methods for text analysis, support vector machines, kernel theory.
In fact, in all periods studied, streets belonging to this location andthose located around it showed cases in the clusters identified by the kernel method and confirmed by Kulldorff.
This work uses kernel methods, probability measures and fuzzy sets, to deal with datasets whose observations are given as sets of points.
After completing his studies, he spent time in Glasgow,where he researched kernel methods for use in discrete event simulation models.
In these areas, the Kernel method can produce values extrapolated to more than 100% or less than 0% seroprevalence, which should be avoided.
The estimate of the areas with the highest prevalence of AIDS cases,based on the application of the Kernel method, showed the existence of spatial clusters Figure 4.
Figure 2 presents the map with the density of cases by the Kernel method, and it is possible to observe that the"hot" areas were concentrated in the vicinities of the central region of the map.
Nonetheless, the initiatives conducted by PHC services are not egalitarian, which helps clusters forms,as shown by the Kernel method and confirmed by the space scan statistic.
In the quantum part,following the heat kernel method in quantum field theory on curved spaces, we derive and study the structure of the 1-loop divergences for the effective action of different models.
Many different in silico approaches for the identification of new drug-target interactions have been proposed,many of them based on a particular class of machine learning algorithms called kernel methods.
For this purpose, we used the Kernel method with adaptive influence radius, which is the ability to describe how the density at one area can influence in its neighborhood- in the case of this study, in neighboring municipalities, taking into account the influence of different geographic areas in Brazilian municipalities.
Thumbnail Figure 1: Surface density of mean community viral load(CVLM) of individuals with AIDS aged 13 years and over,monitored by surveillance systems in Brazil, Kernel method assigning 50 copies/mL for individuals with undetectable viral load.
In this case, there is a strong predominance of spatial questions related to the distribution of the phenomena by space 62.5%, using the spatial smoothing techniques through Moran's index orEmpirical Bayesian Estimation;; and agglomerates based on the Kernel methods;
As regards the methods for spatial analysis used, Kulldorff's spatial scan statistic was found to be useful,because it confirmed that the kernel method can be suitable for a local-level study, once it is a subjective and exploratory method, depending on the researcher's perception to define its parameters.
The punctual intensity of the event was also investigated and examined through the Kernel smoothing method.
In this thesis, we employ the kernel polynomial method(kpm) to calculate the green's functions of non-interacting disordered systems.
Other matching methods used in the evaluation literature are the Kernel matching method and the stratification method. .
There is a buffer underrun vulnerability in the sprintf method of Kernel module.