Sentence examples for mean classification error from inspiring English sources

When wavelet non-linear functions are used in the neurons of a neural network we can estimate the wavelet parameters scale and translation and the relative importance (weight) of each basis function optimally with respect to approximating a given function (signal) in the minimum mean square error sense or with respect to classifying a function (signal) with minimum mean classification error.

The mean classification error rate of the whole classifier structure achieves about 21% for all four emotions and both genders, and the best obtained error rate was 3.5% for the sadness style of the female gender.

We see that for both 15 and 50 features and for moderate to high sample sizes (where we define moderate sample size as for and for, VB outperforms FW and performs similar to BW. For small sample sizes, FW and BW outperform VB. Figure 8 Mean classification error versus log sample size; (a) is for dimensionality, and (b) is for.

The mean classification error was calculated using 10 by 10 cross-validation.

However, this comes at a cost of a larger mean classification error, and as explained above, discarding many SNPs corresponds to discarding the information in those SNPs.

5 Where data have binary outcomes, we used mean classification error (CE), as in Le Cessie and Houwelingen [ 1992]: 6Here, is the estimated probability that the ith individual is a case based on his genotypes, that is.

On the other hand, the method of Schaefer et al., the level set method, and the FCM and FCM-SW methods resulted in substantially lower mean classification errors which varied between 50% (for FCM-SW) and 83% (for FCM) while the EM segmentation approach exhibited a CE of 133.7%.

Following Press et al. [ 82] we calculated the mean variation of the classification error (representing a generalized energy) over 10000 randomly selected chromosomes from the dataset in order to evaluate the starting temperature temp.

In the case of verification on LFW, we have followed the evaluation protocol, and the estimated mean classification accuracy with the standard error ( u ˆ ± SE ) was used for the evaluation.

Since the average classification error is the mean of the two error rates, a majority-classifier that classifies most of the samples as positives would get a high false positive rate and thus a high average classification error.

Second, error function parameter (error.fun) was set to average classification error, which is the mean of false positive rate and false negative rate, instead of the default misclassification rate, which is an overall proportion of all the misclassified samples.