Szczegóły publikacji
Opis bibliograficzny
Probabilistic neural network - parameters adjustment in classification task / Piotr A. KOWALSKI, Maciej Kusy, Szymon Kubasiak, Szymon ŁUKASIK // W: IJCNN 2020 [Dokument elektroniczny] : 2020 International Joint Conference on Neural Networks : [July 19–24, 2020, virtually]. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2020. — (Proceedings of ... International Joint Conference on Neural Networks ; ISSN 2161-4393). — Dod. ISBN Print on Demand(PoD): 978-1-7281-6927-9. — e-ISBN: 978-1-7281-6926-2. — S. [1–8]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [8], Abstr. — Publikacja dostępna online od: 2020-09-28. — P. A. Kowalski, S. Łukasik - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130529 |
|---|---|
| Data dodania do BaDAP | 2020-10-05 |
| Tekst źródłowy | URL |
| DOI | 10.1109/IJCNN48605.2020.9207361 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Joint Conference on Neural Networks 2020 |
| Czasopismo/seria | Proceedings of ... International Joint Conference on Neural Networks |
Abstract
This work presents a comparative analysis of probabilistic neural network training methods applied to achieve best performance in various classification tasks. Two result from classical mathematical methods based on the theory of kernel density estimators: the plug-in method and cross-validation procedure. The other two methods are more advanced: a metaheuristic algorithm of particle swarm optimization, and a procedure based on reinforcement learning. Ten data sets, regarded in eleven classification problems, taken from the UCI repository are used for the numerical analysis. A comparative analysis of probabilistic neural network learning methods leads to interesting conclusions. Although it does not allow for unambiguous selection of the best learning method, it provides a possibility of choosing a method that is adequate for the given conditions. The description of this is included in the work.
