# Minimax deviation strategies for machine learning and recognition with short learning samples

arXiv: https://arxiv.org/abs/1707.04849 · HTML: https://mlsanity.com/paper/1707.04849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Cycle-Consistent Multi-Graph Matching for Self-Supervised Annotation of C.Elegans](https://mlsanity.com/paper/2503.07348/) (2025)

## Abstract (arXiv)

The article is devoted to the problem of small learning samples in machine learning. The flaws of maximum likelihood learning and minimax learning are looked into and the concept of minimax deviation learning is introduced that is free of those flaws.