Supervised classification via neural networks for replicated point patterns

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10468865" target="_blank" >RIV/00216208:11320/23:10468865 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-09034-9_32" target="_blank" >https://doi.org/10.1007/978-3-031-09034-9_32</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-09034-9_32" target="_blank" >10.1007/978-3-031-09034-9_32</a>

Result language
angličtina
Original language name
Supervised classification via neural networks for replicated point patterns
Original language description
A spatial point pattern is a collection of points observed in a bounded region of R^d, d <= 2. Individual points represent, e.g., observed locations of cell nuclei in~a~tissue (d = 2) or centers of undesirable air bubbles in industrial materials (d = 3). The main goal of this paper is to show the possibility of solving the supervised classification task for point patterns via neural networks with general input space. To predict the class membership for a newly observed pattern, we compute an empirical estimate of a selected functional characteristic (e. g., the pair correlation function). Then, we consider this estimated function to be a functional variable that enters the input layer of the network. A short simulation example illustrates the performance of the proposed classifier in the situation where the observed patterns are generated from two models with different spatial interactions. In addition, the proposed classifier is compared with convolutional neural networks (with point patterns represented by binary images) and kernel regression. We consider the kernel regression classifiers for point patterns a benchmark in this setting.
Czech name
—
Czech description
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Project
<a href="/en/project/EF19_073%2F0016935" target="_blank" >EF19_073/0016935: Grant schemes at Charles University</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Classification and Data Science in the Digital Age, Proceedings of the 17th conference of the International Federation of Classification Societies
ISBN
978-3-031-09033-2
ISSN
1431-8814
e-ISSN
2198-3321
Number of pages
8
Pages from-to
—
Publisher name
Springer Cham
Place of publication
neuveden
Event location
Porto
Event date
Jul 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

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