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Interestingly, with intermediate levels of noise, the subregion with high gridness scores (>0.5) retained its crescent-like shape, but was smaller when compared to the networks with theta frequency inputs (size of regions with and without theta: 488/961 vs 438/961), while the range of gamma frequencies present was much lower than in networks containing theta drive.
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a Variable input size.
Figure 10 Input size scaling of WordCount workload.
Let (N) be the input size of the problem.
In the machine learning domain, window size refers to the input size of the prediction algorithm.
Figure 4 Wordcount execution time with respect to requested input size.
Fig. 11 Total run-time (sec) vs. heap and input size scaling.
Nevertheless, depending on the input size, the correntropy can demand a high computational cost.
Data input size is a key factor that impacts performance for Hadoop K-means.
However, increasing the input size makes the prediction model more complex.
However, starting from the input size n=224, we can note that this overhead decreases significantly.
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