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By trading complexity of sensor design for the size of learning set we were able to substantially improve prediction of the SSs.
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If the sizes of learning sets used to develop the expert classifiers are too small, non-trainable fusion rules can outperform more sophisticated trainable ones.
Fuzzy logic is also used to predict the maximum size of learning resources that can be communicated without incurring long delays.
The proven concept was that low baseline knowledge significantly contributed to the size of the learning effect.
Finally, we tested to see if there was a sequence by treatment interaction; i.e., whether the size of the learning effect depended on which form of the Bruce test was taken first.
We used one model with effect size of learning on the CINS-abbr as the response variable and one model with effect size of learning on the cheetah question as the response variable.
Decreased size of learning set causes substantial performance loss for 3' SS sensor, as shown in Figure 6(b).
Its technology reduces the file-size of learning videos, requiring 1/100th of the bandwidth to watch.
Opposite observation were made with 3' SS sensor, where performance is affected both by degree of cross-correlation between learning and test set and the size of the learning set.
Next, we examined the effect of the size of the learning task (i.e. the number of Boolean functions that need to be learned) on the performance of the different methods.
Here the focus is on small groups, with potential applications on groups from three to seven members, which is usually the size of collaborative learning and task-performing groups.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com