A typical example is classification, where the goal is to learn a mapping from input data ({mathbf x }) to output data y, where (y = {1,ldots,C}), with C being the number of classes.
Typical examples are the classification of tumor images or gene expression measurements, the detection of biological signals in DNA, RNA or protein sequences as well as the recognition of hand written digits or faces in images.
Other examples of facilitation of implementation are classification of priorities into themes [ 73], engagement of media in the exercise to increase coverage [ 74], adaptation of global research priorities at regional or national level [ 79] and writing evidence informed policy briefs [ 23, 80].
In multi-label classification, the examples are associated with a set of labels Y ⊆ L. Multi-label classification algorithms can be categorized into two different groups [15]: (i) problem transformation methods, and (ii) algorithm adaptation methods.
Examples are offered, which illustrate a classification of landscape-historical complexes and cultural-historical landscapes for forest regions of European Russia.
Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.
Examples are provided below for each classification, together with their impact on various stakeholders.
Suppose that we have a variable X with values 0, 0.5 and 1, and assume that the classifications for these examples are 0, 1, and 0, respectively.
Once you have those words, the next example is "intent classification" in natural language understanding (NLU), or understanding from a user request what type of task the user wants to achieve (I covered in a recent blog post how the other aspect of NLU, named entity recognition, works).
