Search results
Results from the WOW.Com Content Network
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 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]
Business mathematics comprises mathematics credits taken at an undergraduate level by business students. The course [3] is often organized around the various business sub-disciplines, including the above applications, and usually includes a separate module on interest calculations; the mathematics itself comprises mainly algebraic techniques. [1]
Expected loss is the sum of the values of all possible losses, each multiplied by the probability of that loss occurring. In bank lending (homes, autos, credit cards, commercial lending, etc.) the expected loss on a loan varies over time for a number of reasons. Most loans are repaid over time and therefore have a declining outstanding amount ...
Operations research also uses stochastic modeling and simulation to support improved decision-making. Increasingly, operations research uses stochastic programming to model dynamic decisions that adapt to events; such problems can be solved with large-scale optimization and stochastic optimization methods.
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.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
The 5% Value at Risk of a hypothetical profit-and-loss probability density function. Value at risk (VaR) is a measure of the risk of loss of investment/capital.It estimates how much a set of investments might lose (with a given probability), given normal market conditions, in a set time period such as a day.