Multiple instance learning

Sat, 18 Jul 2026 09:41:54 +0000

Definition

Multiple Instance Learning (MIL) addresses scenarios where data is grouped into ‘bags’ with a single label, while individual instances within those bags remain unlabeled. A bag is typically positive if at least one instance is positive, and negative only if all instances are negative. This technique is crucial when precise labeling of individual data points is costly or impossible, allowing models to learn from coarse-grained supervision signals effectively.

Summary

A weakly supervised learning paradigm where labels are assigned to bags of instances rather than individual instances.

ML Paradigm on English AI Terms Dictionary

Multiple instance learning

Definition

Summary