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Following is the comparison of the framework with a baseline random guessing and an exhaustive framework.
As there are 10 objects, the baseline (random) recognition accuracy is 0.1.
Note that when λ=0, the above relation reduces to the distribution (G x)) of the baseline random variable.
Further, we compared the automated genetic framework performance with a baseline random guessing (Langdon et al. 2016) and an exhaustive framework (Quesada-Lopez et al. 2016).
The analysis of the standardized accuracy (SA) measure revealed that all best learning schemes selected by the genetic framework outperforms the baseline random guessing by 45 80%.
We therefore suggest, as a reasonable baseline, random guessing with probability equal to our prior knowledge of the fraction of explorations: (P exploration) approx 0.2).
Similar(37)
7 The results of our baseline random-effects and fixed-effects models are presented in Table 1, columns 1 & 2. The initial baseline models suggest that the expansion of industrial tree plantations during this period is significantly associated with higher levels of poverty (p < 0.05).
Our starting point is to postulate an unobserved baseline binary random variable.
The specific form reflects the fact that AUROC = 0.5 corresponds to the baseline for random guesses.
At baseline, the random, nationally representative, age and sex stratified sample consisted of 3107 older adults (55 – 85 years).
At baseline, a random subcohort of 5000 participants was selected using a case-cohort approach for analysis.
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