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This paper presents the details of the proposed accelerated algorithm, having a time complexity of O(n log n), that decreases training time significantly without decreasing prediction accuracy.
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But by the time the results of that pilot were known, it had been replaced by a far less generous statewide program that lowered income eligibility, decreased training and incentives, stiffened sanctions and set time limits.
β-stiffness index was also different at PRE (HEDIT PRE: 3.9 ± 1.0 a.u. vs. MEDIT PRE: 4.6 ± 1.1 a.u., p = 0.05) and ANCOVA analysis indicated a significant decrease with training (time effect: p < 0.01, Fig. 2), but no interaction (p = 0.73).
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In addition, the T-test proved that the classic VNOS score decreased with the increase in training time (T = 2.859, p = 0.004), while the modern VNOS score increased with the increase in training time (T = − 2.182, p = 0.029) (Zhang et al. 2013).
As our processing capabilities grow in availability, and our algorithms and statistical tools become more efficient, so-called "training" time for machine learning is decreasing.
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