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For efficient learning, PS-MKL employs a sample selection strategy.
Therefore, we proposed a sample selection strategy for pixel-level classifier training.
Because of the sample selection strategy, this study is not addressed to evaluate the effect of very recent genetic flow from outside populations - essentially from the Italian mainland - that, according to demographic data, occurred mainly during the last sixty years.
The sample selection strategy is described elsewhere.
Sample selection strategy is presented in Supplement 1. Sequence was analyzed with reference to CNTNAP5 messenger RNA, NM_130773.
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Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results.
Hence the false negative rates of RNA tests cannot be evaluated due to the sample selection strategies.
Kang and Marjoram (2012) recently proposed a similar tree-based sample-selection strategy for next-generation sequencing motivated from the standpoint of coalescent theory instead of phylogenetic diversity.
Recently, several studies have investigated a number of theoretical alternatives to stratification for LiDAR-assisted sample plot selection strategies (e.g. Grafström and Ringvall 2013; Grafström et al. 2014).
In the first set of experiments our aim was to investigate the performance of the three sample subset selection strategies described in Section 2. This was done in the context of their ability to minimize information loss expressed by Eq. (17).
The sampling and neighbourhood selection strategy have been described in detail elsewhere.
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