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. 2018 Aug 13;12:49. doi: 10.3389/fnbot.2018.00049

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Copyright © 2018 Tyukin, Gorban, Sofeykov and Romanenko.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Performance of the up-trained AI_s on Testing set 1 (Set 1). Red circles correspond to the original AI_s without Knowledge Transfer Units. Blue squares correspond to AI_s after Algorithm 1, and green triangles illustrate application of Algorithm 2. (A) Shows Precision (averaged over 10 realizations of Set 1) as a function of the decision threshold in AI_s, (B) shows Recall (averaged over 10 realizations of Set 1), (C) contains the corresponding Precision-Recall data, and (D) shows True positives vs. False positives for a single realization of Set 1. Lines between points are shown for the sake of visualization and do not represent any measurements or indicate achievable performance.