Construct · 1 fields · 1 citations

empirical risk minimization: a standalone construct

One name. One construct. A landmark on the map.

● anchor · a standalone construct Nearest construct by meaning·not indexed

A single standalone construct; nearby constructs are located against it in the Collider. Tap a field for its definition, unit, and measures.

Machine learning

the learning principle of choosing the function that minimizes average loss on the training sample as a stand-in for expected loss.

In machine learning, empirical risk minimization refers to the learning principle of choosing the function that minimizes average loss on the training sample as a stand-in for expected loss. This meaning comes from Vapnik (1999) in IEEE Transactions on Neural Networks.[1]

Unit
Recovery means
Measured by

Run it through the Collider, or take the citations with you.

See how empirical risk minimization sits against neighbouring constructs in the Collider, or export every reference on this page in one click.

Open in Collider Export BibTeX Report this entry

Nearby constructs

risk6 fields · 13 citationsjingle · one name, six constructs function5 fields · 6 citationsjingle · one word, five constructs learning6 fields · 14 citationsjingle · one name, six constructs

References

Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988-999. https://doi.org/10.1109/72.788640

Cited on this page?

Confirm your construct, or correct us.

If your work is cited behind one of these entries, you know better than we do whether we captured the meaning right. Confirm it, suggest an edit, or dispute it, and your name goes on the entry as an author who has checked it, linked to your ORCID.

Claim your construct