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Pruning was based on examining the complexity parameter and cross validation costs using the 1-SE rule[ 15].
The same rule also applies to cross validation.
We perform fivefold cross validation.
In the recognition process, we adopt the leave-one-out cross-validation rule to obtain fair recognition rate.
We adopt the leave-one-out cross-validation rule and the cumulative match score (CMS) to evaluate the performance of our method.
We used all the training samples in the AFEW training set and collected training samples from the CK+ and MMI DBs, according to the person-independent tenfold cross-validation rule.
In brief, we trained and tested two classifiers for each local searchlight pattern using 7-fold cross-validation (rule-based aspatialions: spatial vs. non-spatial; low-level visual features: circle pairs 1 and 2 vs. circle pairs 3 and 4; trials per category, mean ± s.d.: 54 ± 5).
The average number of MODLEM decision rules in the five rule sets generated by cross validation is 773 rules, with mean and maximum length of 3.65 and 8 descriptors, respectively.
We performed a cross validation method to test the quality of decision rules.
The tree was pruned according to the one standard error rule with a 50-fold cross validation procedure [ 18].
Table 4 QA performance numbers by tenfold cross-validation Pooling rule SROCC Maximum p% 0.9248 Average 0.9004 Weighted 0.9080 Different pooling rules were applied on the blurred image portion of the LIVE Image Quality Database.
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