Fourth, we propose a method based on multivariate regression to decrease the estimation errors of the number of locked on and off devices with lockout constraints, compared to [12].
For this work, the assumption used considers an inexact estimation for the secondary path which may cause errors on the number of coefficients or on their values as was done in [21].
One of the main advantages of the SVM is to provide an upper band for generalization error based on the number of support vectors in the training set.
Note that total systematic error depends on the number of each type of interaction and thus will not exactly cancel when comparing different protein folds, since the folds may have different numbers of interaction types.
However, here we will try to do a back-of-the-envelope calculation according to our previous work based on the in vitro characterization of Dendra2 photophysics, where we demonstrated that the counting error depended on the number of molecules and the length of the experiment.
In the present paper, we aim to establish how this baseline rate of type I errors depends on the number of predictors including interactions (k) in the initial full model and on sample size (N).
Self-organizing in structure learning is accomplished through a new measure that depends on error, the number of rules, and validity degrees.
As shown in Fig. 4 and to give more visibility to Table 5 we can notice the influence of similarity on the error rate of the recognition system, we can, therefore, note that: Fig. 4 Error rate based on the number of records in the SVM method compared to other methods 1.
Often researchers plot participant means and standard error (as based on the number of participants), which, while potentially representative of the data used for an ANOVA, do not match the data used for a mixed effects model.
