optimal selection - definição. O que é optimal selection. Significado, conceito
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O que (quem) é optimal selection - definição

CLASS OF MATHEMATICAL PROBLEMS CONCERNED WITH CHOOSING AN OPTIMAL TIME TO TAKE A PARTICULAR ACTION
Optimal Stopping; Optimal Stopping problem

Feature selection         
  • Embedded method for Feature selection
  • Wrapper Method for Feature selection
  • Filter Method for feature selection
PROCEDURE IN MACHINE LEARNING AND STATISTICS
Input selection; Feature selection problem; Variable selection; Feature subset selection
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons:
Material selection         
  • '''Figure 3.''' Ashby chart with performance indices plotted for maximum result
SELECTION PROCESS OF A MATERIAL FOR A PARTICULAR APPLICATION
Materials selection
Material selection is a step in the process of designing any physical object. In the context of product design, the main goal of material selection is to minimize cost while meeting product performance goals.
Fecundity selection         
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MODE OF NATURAL SELECTION
Fertility selection
Fecundity selection, also known as fertility selection, is the fitness advantage resulting from the preference of traits that increase the number of offspring (i.e.

Wikipédia

Optimal stopping

In mathematics, the theory of optimal stopping or early stopping is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost. Optimal stopping problems can be found in areas of statistics, economics, and mathematical finance (related to the pricing of American options). A key example of an optimal stopping problem is the secretary problem. Optimal stopping problems can often be written in the form of a Bellman equation, and are therefore often solved using dynamic programming.