Examples of using Error function in English and their translations into Chinese
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This is the error function.
It should be noted that each differenttype of modulation has its own value for the error function.
The total error function is then given by.
Where erf(x) is the error function.
A natural choice of error function would be the total number of misclassified patterns.
Where erfc(x) is the complementary error function.
The cross-entropy error function is defined as follows:.
Level 1(the default), the error position is where the error function was called.
The complementary error function is defined as 1.0- erf(x).
When we are minimizing it, we may also call it the cost function, loss function, or error function.
Calculus of variations to minimize an error function and produce a stable solution.
The hinge error function, so-called because of its shape, is plotted in Figure 7.5.
We shall discuss the motivation for this choice of error function later in this chapter.
The error function is used to produce an ErrorException that interrupts the normal flow of control.
We therefore consider an alternative error function known as the perceptron criterion.
Note that the error function in this case is quadratic and hence the Newton-Raphson formula gives the exact solution in one step.
However, network error depends on every network weight and the error function is much, much more complex.
Note that this simply multiplies the error function by a factor of 2 and so is equivalent to using the original error function. .
The problem with this approach is that this way,we can hit a local minimum of the error function, but not the global one.
It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization.
In a lot of cases, choosing the appropriate model comes down to the design choices of divergence measurement,reconstruction error function, and imposed priors.
Of course, this does not imply that the contribution to the error function from the other misclassified patterns will have been reduced.
The coverage_error function computes the average number of labels that have to be included in the final prediction such that all true labels are predicted.
As we shall see in Section 5.3,it is possible to evaluate the gradient of an error function efficiently by means of the backpropagation procedure.
When we are minimizing it, we may also call it the cost function, loss function, or error function.
And known as the erf function or error function(not to be confused with the error function of a machine learning model).
This paper describes the general structure of such problems and presents gopt,an open-source C++ framework for optimizing graph-based nonlinear error functions.
(2003) found that using the cross-entropy error function instead of the sum-of-squares for a classification problem leads to faster training as well as improved generalization.
In mathematics, the error function(also called the Gauss error function) is a non-elementary function which occurs in probability, statistics and partial differential equations….