Examples of using Kernel function in English and their translations into Portuguese
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The man pages in section 9- for important documentation on kernel functions.
For the normal kernel function estimate it were used the following influence radius: 100m, 150m, 200m and 500m.
In particular, a 5-byte call instruction is injected to the start of kernel functions.
Despite this, sleeping processes that use the old kernel functions are not considered a security issue.
A range of kernel functions are commonly used: uniform, triangular, biweight, triweight, Epanechnikov, normal, and others.
The same is based on the method of complex images andpropose a finite number of complex exponentials to represent the kernel function.
For some kernel functions, matrix algebra can be used to calculate the predictions using the technique of kriging.
This work extends a probabilistic method of classic data clustering to symbolic interval data using of kernel functions.
On the other hand, for the quartic kernel function estimate it were used the following influence radius: 250m, 375m, 500m and 625m.
The former are based in a functional regressor estimator proposed by ferraty e vieu(2006)that includes kernel functions to do local weighting between the functional variables.
The selection of the right kernel function and its respective parameters can have a large influence on the performance of the classifier.
This is implemented differently by each kernel, but most provide a C library or an API,which in turn invokes the related kernel functions.
Upon termination, the script will display a list of kernel functions and timing information, sorting the output in increasing order of time.
The support vector classification(svc) algorithm is a pattern recognition technique, whose efficiency depends on its parameters selection:the penalty constant c, the kernel function and its own parameters.
To patch a kernel function, kGraft needs some space at the start of the function to insert a jump to a new function. .
The generalization ablity of¿-svr and¿-sve were compared,as well as the performance of following kernel functions: linear, polinomial, gaussian radial basis function(rbf) and sigmoid.
However, invocations of the old kernel functions are not completely eliminated until all sleeping processes wake up and get out of the way.
Broadly speaking a singular integral is an integral operator T( f)( x)∫ K( x, y) f( y) d y,{\displaystyle T(f)(x)=\int K(x, y)f(y)\, dy,}whose kernel function K: Rn×Rn→ R is singular along the diagonal x y.
The application of kernel functions have been successfully used in classic data clustering showing positive results when the data set has non linearly separable groups.
Barring the scheduler from preempting tasks while they are processing kernel functions simplifies the kernel design at the expense of system responsiveness.
This usually applies to kernel functions and service interrupts which, if not permitted to run to completion, would tend to produce race conditions resulting in deadlock.
This work has the objective to develop an analytical model that makes prediction of the behavior of the algorithm as a function of the design parameters step adaptation, kernel function and its parameters.
The predictions¿estimation were obtained by applying 9 different kernel functions extracted from the scientific literature, resulting in a total of 135 models verified.
It applies a kernel function in the cost function from the non-recursive quadratic function of an even power, which minimizes the error, defined as the expectation of the cumulative cost of actions taken along a sequence of steps.
A machine-learning algorithm that involves a Gaussian process uses lazy learning anda measure of the similarity between points(the kernel function) to predict the value for an unseen point from training data.
The award is granted for mathematical research in:"1 the theory of the kernel function and its applications in real and complex analysis; or 2 function-theoretic methods in the theory of partial differential equations of elliptic type with attention to Bergman's operator method."The award is given in honor of Stefan Bergman.
Triangular functions are useful in signal processing and communication systems engineering as representations of idealized signals, and the triangular function specifically as an integral transform kernel function from which more realistic signals can be derived, for example in kernel density estimation.
The SVM technique seeks a plane that has the same distance for elements of both classes,using a Kernel function which calculates the classification function for sets in which the data are not linearly separable.
When using dichotomous variables with a value assigned to the points,as is the case for the serological status of HIV, the Kernel function is considered according to the variance of the values presented in each position in relation to the region studied.