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It's not that the Socratic method is worse than technology-based learning or even imperiled by it.
More importantly, we note that the performance of the original method is worse than that of BD above an SNR of 16 dB.
Compared to the Simple method that regards our segmented block as a unit, the Simple w/o Block method is worse by 2.6%% in terms of (F_1) measure and has to submit 4 more queries on average.
We have also noticed that, while the existing SZF covariance method is worse than our proposed CGP method, the covariance matrices Q k,o provided by the existing method sometimes provide a better starting point for our CGP than the round-robin initialization.
Also, the theoretical FCI method is worse than the CAPC-1 method in the low SNR regime since it allocates more power to the deep fades whereas the CAPC-1 method cannot invert the channel totally, making it actually a truncated algorithm.
For facilitating the discussion, we denote the conventional recommendation methods, i.e., Random, Popular, User-CF, Item-CF, SVD++, and LFM, as Group A and MIML style methods as Group B. The counts that each Group B method is better than all among the Group A approaches, and the counts that each Group B method is worse than any of the Group A methods, are listed as well.
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However, their method was worse than such greedy solutions for unconstrained problems.
Only for the Image Segmentation and Diabetes data sets, the accuracy of the proposed method was worse.
From Figure 2a,c, it can be seen that gain and position error estimation performances of the conventional method are worse than those of the modified method.
However, concentration predictions by the pilot ion method were worse for less than 15 μM Fe(II) (46% different on average).
In particular, the relative performance of the PhyML method was worse when the topology contained pectinate branching.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com