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Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy Read chapter Read first chapter

2018 | OriginalPaper | Chapter

An Incremental Machine Learning Algorithm for Nuclear Forensics

Author : Chris Drummond

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

This paper presents an incremental machine learning algorithm that identifies the origin, or provenance, of samples of nuclear material. This is part of work being undertaken by the Canadian National Nuclear Forensics Library development program, which seeks to build a comprehensive database of signatures for radioactive and nuclear materials under Canadian regulatory control. One difficulty with this application is the small ratio of the number of examples over the number of classes. So, we introduce variants to a basic generative algorithm, based on ideas from the robust statistics literature and elsewhere, to address this issue and to improve robustness to attribute noise. We show experimentally the effectiveness of the approach, and the problems that arise, when adding new examples and classes.

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Metadata
Title
An Incremental Machine Learning Algorithm for Nuclear Forensics
Author
Chris Drummond
Copyright Year
2018
Book
Advances in Artificial Intelligence

Print ISBN: 978-3-319-89655-7

Electronic ISBN: 978-3-319-89656-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-89656-4_16

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