Cost Function
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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.
- Cost function can refer to:
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).
- QUOTE: ... Graph edit distance and maximum common subgraph are well known concepts that have various applications in pattern recognition and machine vision. In this paper a particular cost function for graph edit distance is introduced, and it is shown that under this cost function graph edit distance computation is equivalent to the maximum common subgraph problem. …