# Cost Function

(Redirected from Loss Function)

A Cost Function is an utility function that is used in a minimization task.

**AKA:**Loss Function.**Context:**- It can range from being a Linear Cost Function to being a Non-Linear Cost Function.
- It can rank decision values within a range of lower cost and higher cost.

**Example(s):**- a Cost Vector.
- an Economic Cost Function.
- a Parameter Fitting Loss Function, such as a learning loss function.

**Counter-Example(s):****See**Cost Function Optimization, Cost-Sensitive Classification.

## References

### 2014

- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/cost_function Retrieved:2014-4-3.
**Cost function**can refer to:- In economics, the cost curve, expressing production costs in terms of the amount produced
- In mathematical optimization, the loss function, a function to be minimized
- In artificial neural networks, the function to return a number representing how well the neural network performed to map training examples to correct output.

### 2011

- (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Loss Function.” In: (Sammut & Webb, 2011) p.231
- QUOTE: A loss function is a function used to determine loss.

### 2006

- (Li & Link 2006) ⇒ Ling Li, and Hsuan-Tien Lin. (2006). “Ordinal Regression by Extended Binary Classification.” In: Advances in Neural Information Processing Systems 19 (NIPS 2006).

### 1997

- (Bunke, 1997) ⇒ Horst Bunke. (1997). “On a Relation Between Graph Edit Distance and Maximum Common Subgraph.” In: Pattern Recognition Letters, 18(9).