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With the dynamic development of modern imaging methods, large datasets of point patterns are available representing for example sub-cellular location patterns for human proteins or large forest populations. The main goal of this paper is to show the possibility of solving the supervised multi-class classification task for this particular type of complex data via functional neural networks. To predict the class membership for a newly observed point pattern, we compute an empirical estimate of a selected functional characteristic. Then, we consider such estimated function to be a functional variable entering the network. In a simulation study, we show that the neural network approach outperforms the kernel regression classifier that we consider a\u00a0benchmark method in the point pattern setting. We also analyse a real dataset of point patterns of intramembranous particles and illustrate the practical applicability of the proposed method.<\/jats:p>","DOI":"10.1007\/s11634-024-00579-5","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T09:02:17Z","timestamp":1707296537000},"page":"705-721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural networks with functional inputs for multi-class supervised classification of replicated point patterns"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0065-2215","authenticated-orcid":false,"given":"Kate\u0159ina","family":"Pawlasov\u00e1","sequence":"first","affiliation":[]},{"given":"Iva","family":"Karafi\u00e1tov\u00e1","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3290-8518","authenticated-orcid":false,"given":"Ji\u0159\u00ed","family":"Dvo\u0159\u00e1k","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"579_CR1","doi-asserted-by":"crossref","unstructured":"Allaire J, Eddelbuettel D, Golding N, et\u00a0al (2016) Tensorflow: R Interface to TensorFlow. https:\/\/github.com\/rstudio\/tensorflow","DOI":"10.32614\/CRAN.package.tensorflow"},{"key":"579_CR2","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.spasta.2018.04.009","volume":"27","author":"I Andersen","year":"2018","unstructured":"Andersen I, Hahn U, Arnspang E et al (2018) Double Cox cluster processes\u2014with applications to photoactivated localization microscopy. 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