In general, there are different conceptual frameworks for the classification of learning environments in the classroom and for differentiating activities and cognitive processes during learning.
The interview data were categorized retaining the classification of learning strategies (Gargallo et al. 2009) and, from these, the following scheme was generated: (Fig. 1).
We opted for O'Malley and Chamot's (1990) classification of learning strategies, and, more specifically, for Vandergrift's (1997), Flowerdew and Miller's (2005), and Vandergrift and Gogh's (2012) adaptation of such a classification to listening.
Nevertheless, unlike them, our study focuses on the effect of the implemented instruction on all the strategies included in the classification of learning strategies developed by O'Malley and Chamot (1990) and adapted to listening by Vandergrift (1997), Flowerdew and Miller (2005), and Vandergrift and Gogh (2012).
As expected, the resulting classification of learning set samples strongly correlated with clinical outcome (P=8.5E−04, Supplementary Figure S2A).
From this viewpoint, the task of protein functional properties prediction can be cast into the collective classification problem of learning a predictive model from PPI networks.
In such cases, the classification task consists of learning the optimal discrimination boundary along Toxin A axis that maximizes the discriminatory power of the classifier at the level below 100% accuracy.
Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets.
