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Figure 8 Comparison with other methods in average of all categories.
This figure shows the results of comparison between our hybrid method and basic methods in average of all categories of java online community.
Figure 7 indicates comparison between our hybrid method and basic methods in different categories of java online community and Figure 8 indicates comparison between our hybrid method and basic methods in average of all categories of java online community.
We have the following observations from the experimental results: (1) Our HierSymNMF2 algorithm has significant advantages over other methods in average normalized cut on most datasets except the Amazon dataset.
As you see in Figure 7 our hybrid method is better than other methods in most of categories of java online community, in addition as you see in Figure 8 our method is better than other methods in average of all categories of java online community.
There were no significant differences between BayesA, BayesCπ and GBLUP methods in average accuracy or persistency of accuracy across traits.
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GP registrars' evaluation of the learning methods was in average positive.
Specifically, we considered the standard deviation of average motif correlations (SAMC) of a hub (see Materials and Methods), in which average motif correlation (AMC) of a network motif is the average value of Pearson correlation coefficients between two proteins connected in the network motif.
The comparative results show that for the larger dataset, DCF-DLPE outperforms the DES, AES (256-bit) and Blowfish encryption methods in the average time-consumption.
The simplex method is the most commonly used [17]; despite its worst-case exponential time, this method is in average very efficient [18].
We also observe that the errors for the PMCMC method are, in average, only 12% (excitatory) and 8% (inhibitory) worse than the errors for the PF method.
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