‚Ä¶sample count of 1% of the input samples and regression accuracy equal to 0.1 with termination criteria equal to 50 trees in the forest, and ‚Ä¢ Multiclass AdaBoosting (MAB), as described by Friedman et al. (2000), with 100 weak classifiers, trim rate equal to 0.95, and maximal depth equal to 5.
Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method
Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background



Sample text:```Our heritability result is generally in agreement with the studies of Kien et al. (2008) in Eucalyptus urophylla and Zhao et al. (2014a) in poplar.```
Sample intent: result



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result
Sample text:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
Sample intent: background



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```Only one published paper (Enock et al. 2014) has examined this question, and thatwas in the context of anABM training study.```
Sample intent: method



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
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Sample text:```Apical color M-mode recordings were obtained at a depth of 16 to 18 cm and a Nyquist limit of 18 to 25 cm/s (15).```
Sample intent: method



Sample text:```The methods working directly on the count data can be broadly divided into parametric (baySeq [14], EBSeq [24], ShrinkSeq [27], edgeR [23], DESeq [7], NBPSeq [15] and TSPM [13]) and non-parametric methods (NOISeq [25] and SAMseq [26]).```
Sample intent: background



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method