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In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. [4]
In mathematics, a constraint is a condition of an optimization problem that the solution must satisfy. There are several types of constraints—primarily equality constraints, inequality constraints, and integer constraints. The set of candidate solutions that satisfy all constraints is called the feasible set. [1]
A regularization term (or regularizer) () is added to a loss function: = ((),) + where is an underlying loss function that describes the cost of predicting () when the label is , such as the square loss or hinge loss; and is a parameter which controls the importance of the regularization term.
def – define or definition. deg – degree of a polynomial, or other recursively-defined objects such as well-formed formulas. (Also written as ∂.) del – del, a differential operator. (Also written as.) det – determinant of a matrix or linear transformation. DFT – discrete Fourier transform.
The squared loss has the disadvantage that it has the tendency to be dominated by outliers—when summing over a set of 's (as in = ()), the sample mean is influenced too much by a few particularly large -values when the distribution is heavy tailed: in terms of estimation theory, the asymptotic relative efficiency of the mean is poor for heavy ...
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). [1]
Rigor is a cornerstone quality of mathematics, and can play an important role in preventing mathematics from degenerating into fallacies. well-behaved An object is well-behaved (in contrast with being Pathological ) if it satisfies certain prevailing regularity properties, or if it conforms to mathematical intuition (even though intuition can ...
may mean that A is a subset of B, and is possibly equal to B; that is, every element of A belongs to B; expressed as a formula, ,. 2. A ⊂ B {\displaystyle A\subset B} may mean that A is a proper subset of B , that is the two sets are different, and every element of A belongs to B ; expressed as a formula, A ≠ B ∧ ∀ x , x ∈ A ⇒ x ∈ ...