Supervised classification via neural networks for replicated point patterns

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Supervised classification via neural networks for replicated point patterns

Popis výsledku v původním jazyce

A spatial point pattern is a collection of points observed in a bounded region of R^d, d &lt;= 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.

Název v anglickém jazyce

Supervised classification via neural networks for replicated point patterns

Popis výsledku anglicky

A spatial point pattern is a collection of points observed in a bounded region of R^d, d &lt;= 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/EF19_073%2F0016935" target="_blank" >EF19_073/0016935: Grantová schémata na UK</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

Springer Cham

Místo vydání

neuveden

Místo konání akce

Porto

Datum konání akce

19. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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