error function

Note that the error function's value at is equal to 1.

It also appears in the definition of the error function.

However, the final potential function is not the 0-1 loss error function.

The model is matched by the minimization of an error function.

The error function at + is exactly 1 (see Gaussian integral).

This standard error function of p has a maximum at .

The property means that the error function is an odd function.

Most error functions relate to two aspects of visual servoing.

The larger the error function, the larger the difference between the expected and actual reward.

A continued fraction expansion of the complementary error function is:

The second element of the algorithm was the interpolation step needed to evaluate the error function.

The derivative of the error function follows immediately from its definition:

"On a distribution yielding the error functions of several well known statistics" Proc.

This usage is similar to the Q-function, which in fact can be written in terms of the error function.

The error function reports back the difference between the estimated reward at any given state or time step and the actual reward received.

It is a scaled form of the complementary Gaussian error function:

Temporally, this wavelet can be expressed in terms of the error function, as:

This mimics closely how the error function in TD is used for reinforcement learning.

Details for computing the inverse error function can be found at [1].

As a quick approximation of the error function, the first 2 terms of the Taylor series can be used:

The Fresnel integrals can be expressed using the error function as follows:

Regularization can be used to fine tune model complexity using an augmented error function with cross-validation.

"I would suggest, Captain, that you program the tricorder to take an average of several readings and report the error function along with the median value."

This approximation can also be inverted to calculate the inverse error function:

Therefore, we can define the error function in terms of the incomplete Gamma function: