Szczegóły publikacji
Opis bibliograficzny
Triggering Probabilistic Neural Networks with Flower Pollination Algorithm / Piotr A. KOWALSKI, Konrad Wadas // W: Computational intelligence and mathematics for tackling complex problems / eds. László T. Kóczy, [et al.]. — Cham : Springer, cop. 2020. — (Studies in Computational Intelligence ; ISSN 1860-949X ; 819). — Publikacja zawiera materiały z konferencji ESCIM 2018 : tenth European Symposium on Computational Intelligence and Mathematics, 7-10 October 2018, Riga, Latvia. — ISBN: 978-3-030-16023-4; e-ISBN: 978-3-030-16024-1. — S. 107-113. — Bibliogr., Abstr. — Publikacja dostępna online od: 2019-05-03. — P. A. Kowalski - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 122347 |
|---|---|
| Data dodania do BaDAP | 2019-10-29 |
| DOI | 10.1007/978-3-030-16024-1_14 |
| Rok publikacji | 2020 |
| Typ publikacji | fragment monografii pokonferencyjnej |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Studies in Computational Intelligence |
Abstract
The Flower Pollination Algorithm (FPA) is a modern heuristic technique that is applicable for the purposes of deriving best solution within several optimization tasks. This paper is a example of utilizing this metaheuristic procedure for the Probabilistic Neural Network (PNN) learning process. In this paper, for the purpose of classification, this type of Neural Network is applied to data sets drawn from the UCI Machine Learning Repository. Moreover, we concentrate upon investigating the inertial parameters of FPA, as well as the overfitting aspect. © Springer Nature Switzerland AG 2020.
