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:
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• See: Empirical Loss Function, Expected Value.

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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.

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]\displaystyle{ 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.

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