Loss Functions in Machine Learning

Status: public · Confidence: medium (0.84) · Basis: verified_sources

## TL;DR

Loss functions define optimization targets for machine learning, from cross-entropy to robust and class-imbalance losses. This repair keeps only source-backed examples.

## Core Explanation

The previous entry had weak source matching. The repaired version maps core loss-function claims to textbook or paper evidence.

## Further Reading

- [Deep Learning](https://www.deeplearningbook.org/contents/ml.html)
- [Robust Estimation of a Location Parameter](https://doi.org/10.1214/aoms/1177703732)
- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)