Probability Distribution as an Input to Machine Learning Tasks

Karel Macek, Nicholas Čapek, Nikola Pajerová

2023

Abstract

Machine Learning has been working with various inputs, including multimedia or graphs. Some practical applications motivate using unordered sets considered to be samples from a probability distribution. These sets might be significant in size and not fixed in length. Standard sequence models do not seem appropriate since the order does not play any role. The present work examines four alternative transformations of these inputs into fixed-length vectors. This paper demonstrates the approach in two case studies. In the first one, pairs of scans as coming from the same document based were classified on the distribution of lengths between the reference points. In the second one, the person’s age based on the distribution of D1 characteristics of the 3D scan of their hip bones was predicted.

Paper Citation

in Harvard Style

Macek K., Čapek N. and Pajerová N. (2023). Probability Distribution as an Input to Machine Learning Tasks. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 123-129. DOI: 10.5220/0011766500003467

in Bibtex Style

@conference{iceis23,
author={Karel Macek and Nicholas Čapek and Nikola Pajerová},
title={Probability Distribution as an Input to Machine Learning Tasks},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={123-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011766500003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Probability Distribution as an Input to Machine Learning Tasks
SN - 978-989-758-648-4
AU - Macek K.
AU - Čapek N.
AU - Pajerová N.
PY - 2023
SP - 123
EP - 129
DO - 10.5220/0011766500003467
PB - SciTePress