Sentence examples for popular classifications from inspiring English sources

Popular classifications, however, remain useful tools for studying the common stresses that the environment exerts on all plants and the general patterns of adaptation that are shown no matter how distantly plants are related.

At the same time, by an analysis of currently popular classifications, such as "cultural landscape", we put forward new definitions of World Heritage categories, and the position it is proposed they take in the overall classification system.

Popular classifications are "missing completely at random" (MCAR – the missing values are independent of both observed and unobserved data); "missing at random" (MAR – the missing values are independent of unobserved data but may depend on observed data) and "not missing at random" (MNAR – the missing data depends on both observed and unobserved data).

These are the most popular classifications for nonprofits, but there are many others.

Another popular classification is that of R.L. Folk (1974).

This enhanced GONN algorithm produces better results than popular classification algorithms like Genetic Algorithm, Support Vector Machine and Neural Network which makes it a good alternative to the well-known machine learning methods for solving multi-class classification problems.

For the second case, it is shown that the sweet sub-note considered in Aldrich's catalog is not a relevant sub-note, confirming the alternative and popular classification of Jaubert et al. [J.N. Jaubert, C. Tapiero, J.C. Dore, The field of odour: toward a universal language for odour relationships, Perfumer Odourist 20 (2) (1995) 1 16], the field of odours.

For pattern classification, three popular classification algorithms are applied, which are SVM, k-NN, naïve Bayes.

Fisher's discriminant analysis (Fisher's LDA) is a very popular classification algorithm in BCI research [19].

In this article, we will utilize three popular classification techniques, namely, naïve Bayes, SVMs, and k-nearest neighbor (k-NN), to evaluate the proposed distance-based features.

Nevertheless we attempted three most popular classification algorithms used in machine learning approaches; NB [48], RF [49] and PNN [50] as shown in Fig. 1.