SUPERVISED NONPARAMETRIC CLASSIFICATION IN THE CONTEXT OF REPLICATED POINT PATTERNS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10447848" target="_blank" >RIV/00216208:11320/22:10447848 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=AQ0SX49DqO" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=AQ0SX49DqO</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5566/ias.2652" target="_blank" >10.5566/ias.2652</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SUPERVISED NONPARAMETRIC CLASSIFICATION IN THE CONTEXT OF REPLICATED POINT PATTERNS

Popis výsledku v původním jazyce

A spatial point pattern is a collection of points in space, representing, e.g. observed locations of trees, bird nests, centers of cells in a histological sample, etc. When several independent realizations of the underlying stochastic process are observed, these realizations are referred to as replicated point patterns. The main objective of this paper is to classify a newly observed pattern into one of the existing classes using a supervised nonparametric classification method, namely the Bayes classifier in combination with the k-nearest neighbors algorithms and the kernel regression method. The dissimilarity between a pair of patterns is defined using the functional summaries extracted from the point patterns via the Cramer-von Mises or Kolmogorov-Smirnov type formula. A set of simulation experiments is presented to investigate the performance of the proposed classifier with a dissimilarity measure based on functional summaries, such as the pair correlation function. The application of such a classifier to a real point pattern dataset is also illustrated.

Název v anglickém jazyce

SUPERVISED NONPARAMETRIC CLASSIFICATION IN THE CONTEXT OF REPLICATED POINT PATTERNS

Popis výsledku anglicky

A spatial point pattern is a collection of points in space, representing, e.g. observed locations of trees, bird nests, centers of cells in a histological sample, etc. When several independent realizations of the underlying stochastic process are observed, these realizations are referred to as replicated point patterns. The main objective of this paper is to classify a newly observed pattern into one of the existing classes using a supervised nonparametric classification method, namely the Bayes classifier in combination with the k-nearest neighbors algorithms and the kernel regression method. The dissimilarity between a pair of patterns is defined using the functional summaries extracted from the point patterns via the Cramer-von Mises or Kolmogorov-Smirnov type formula. A set of simulation experiments is presented to investigate the performance of the proposed classifier with a dissimilarity measure based on functional summaries, such as the pair correlation function. The application of such a classifier to a real point pattern dataset is also illustrated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-04412S" target="_blank" >GA19-04412S: Nové přístupy k modelování a statistice náhodných množin</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Image Analysis and Stereology

ISSN

1580-3139

e-ISSN

—

Svazek periodika

41

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SI - Slovinská republika

Počet stran výsledku

18

Strana od-do

57-74

Kód UT WoS článku

000824607400001

EID výsledku v databázi Scopus

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