Sentence examples for sample and feature from inspiring English sources

Therefore, L2ECM is clearly not appropriate for embedded platforms; hence, for the sake of simplicity, we were much more inclined to use a very simple approach and compute a single covariance descriptor for every sample and feature combination.

The model of MiRNAClassify was constructed by using the same sample and feature sets with PlantMiRNAPred.

The information about the sample and feature of each compared method is listed as follows.

The basic strategy of the proposed method was to iteratively partition in two ways sample and feature space with super-paramagnetic clustering technique and to seek for hard and robust gene clusters that lead to a natural partition of disease samples and that have the highest functionally conceptual consensus evaluated with Gene Ontology.

Han et al. (2012) mentioned that sampling and feature subset selection are done in the data preprocessing phase.

The combination of random sampling and feature selection techniques were used to ensure the necessary diversity level of classifiers at the first step.

From Table 2, we observe that the uniform sampling performs better than the random sampling both when is considered alone and when is coupled with other sampling strategies, i.e., with the probAPD-based sampling and feature detectors.

The sample selection and feature ensemble approaches perform equally as well in head-to-head tests.

The complete approach is the best performing, followed by sample selection and feature ensemble approaches, and the baseline approach.

For one thing, in terms of each compared method, we choose its sample set and feature set to train MiRNAClassify.

In order to compare with microPred in a fair way, we used the sample set and feature set of microPred to train the model of MiRNAClassify.