Loss function and Optimization problem

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Loss function and Optimization problem

Loss function vs. Optimization problem

In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions.

Similarities between Loss function and Optimization problem

Loss function and Optimization problem have 1 thing in common (in Unionpedia): Real number.

Real number

In mathematics, a real number is a value of a continuous quantity that can represent a distance along a line.

Loss function and Real number · Optimization problem and Real number · See more »

The list above answers the following questions

What Loss function and Optimization problem have in common
What are the similarities between Loss function and Optimization problem

Loss function and Optimization problem Comparison

Loss function has 80 relations, while Optimization problem has 48. As they have in common 1, the Jaccard index is 0.78% = 1 / (80 + 48).

References

This article shows the relationship between Loss function and Optimization problem. To access each article from which the information was extracted, please visit:

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