Model-Based Multiple Instance Learning

2017/03/07 by Ba‐Ngu Vo, Dinh Phung, Vo, Ba-Ngu +5 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1703.02155

openalex publication_date 2017/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Fast Learning from Sparse Data
• Survey of Clustering Algorithms

Cited by

• Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations

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