Additionally, this error serves as a rough upper bound for methods which perform pure 3D tracking.
It is also worth noting here that the HF errors (spatial high-frequency component) typically correspond to the reconstruction error in the undersensing case, while the LF error serves as a sanity check for detection.
This error serves as the input to the LMS algorithm, and by adaptively minimizing the localization error, we obtain η as follows: eta(t)=eta t-1 -mu_{eta}frac{partial varepsilon(t)^{2}}{partial eta} (13).
The mean error served as success criterions along with a maximal number of epochs.
The average residual transfer error serves as a quantitative measure of the registration process.
The holdout classification error serves as a measure of the model's predictive (generalization) ability.
In addition, the small random error served as a method to protect confidentiality of the participants.
Those who had no symptoms of dry eyes and no other ocular problems other than refractive error served as controls (16 eyes).
Although the sum-of-squares error serves as the measurement objective function, root-mean-square (RMS) error: (5) is reported in the analyses presented here because RMS is more comparable to water-level measurements.
Finally, the authors propose a technique that has received the largest attention: "DNA molecules could be synthesized to record patterns...of spikes in each cell, encoded as calcium-induced errors, serving as a 'ticker-tape' record of the activity of the neuron".
