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Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks.
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The novelty of the approach is to focus in prototype selection for regression problems instead of classification as the majority of the literature deals only with the last one.
The authors proposed a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes in [15].
First, take the selection problem.
This is an "adverse selection" problem.
And they solve the adverse selection problem.
Prototype selection, manufacturing limitations, constructability constraints, and testing decisions are presented, alongside details related to model construction.
The developments leave Gatland with a potential selection problem.
The index selection problem is first formulated and four index selection algorithms are evaluated via simulation.
Work given in [15] is about prototype selection and [16] is concerning about view selection.
Consequently, we had to consider the prototype selection for each pair of sounds, as follows.
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