# Expected Loss Function

An Expected Loss Function is an estimation function that estimates an expected error over a training set and a testing set.

**Context:**- …

**See:**Empirical Loss Function, Expected Value.

## References

### 2009

- (Chen et al., 2009) ⇒ Bo Chen, Wai Lam, Ivor Tsang, and Tak-Lam Wong. (2009). “Extracting Discrimininative Concepts for Domain Adaptation in Text Mining.” In: Proceedings of ACM SIGKDD Conference (KDD-2009). doi:10.1145/1557019.1557045
- We theoretically analyze the
**expected error**in the target domain showing that the error bound can be controlled by the**expected loss**in the source domain, and the embedded distribution gap, so as to prove that what we minimize in the objective function is very reasonable for domain adaptation.

- We theoretically analyze the

### 2009

- http://en.wikipedia.org/wiki/Loss_function#Expected_loss
- As the result of the decision rule depends on the outcome of a random variable [math]X[/math], the value of the loss function itself is a random quantity. Both frequentist and Bayesian statistical theory involve making a decision based on the expected value of the loss function: however this quantity is defined differently under both paradigms.