# Model-Based Multiple Instance Learning

arXiv: https://arxiv.org/abs/1703.02155 · HTML: https://mlsanity.com/paper/1703.02155/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework](https://mlsanity.com/paper/2108.12159/) (2021)
- [Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs](https://mlsanity.com/paper/2408.07394/) (2024)
- [Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework](https://mlsanity.com/paper/2102.01882/) (2021)
- [Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations](https://mlsanity.com/paper/2506.23802/) (2025)

## Abstract (arXiv)

While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning. This article proposes a framework for model-based MI learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed.