03194ntm a22005177i 4500 000725231 CZ-PrVSE 20250824103944.0 m d cr n|||||||||| 250824s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Vinogradov, Grigorii ISIS:172093 dis Regularizační metody v Probabilistic Neural Networks a General Regression Neural Networks. eng Regularization methods in Probabilistic Neural Networks and General Regression Neural Networks. / Grigorii Vinogradov 2025 ?? stran : digital, PDF soubor Vedoucí práce: Karel Šafr Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 This thesis investigates the application of regularization techniques in general regression neural networks (GRNN) and probabilistic neural networks (PNN). L1 and L2 regularization has been integrated into GRNN and PNN architectures using a Bayesian formulation of parameter estimation. In contrast to the standard kernel density estimation (KDE) approach, in this work the models were reformulated using a mixture of Gaussians for density estimation, allowing the models to move from a non-parametric to a parametric formulation with minimal changes in interpretation. The models were implemented in Python and their performance was evaluated on real-word datasets. In regression tasks, L1 and L2 regularization led to improvements in model performance, with mean square error reductions in the range of 3,02% to 4,04%. In classification tasks, accuracy improvements ranged from 0,36% to 4,44%. However, a slight performance decrease was observed in some datasets, suggesting that the effect of regularization is data-dependent. Způsob přístupu: Internet datové analýzy a modelování [obor bakal. práce] bakalářské práce fd132403 czenas bachelor's theses eczenas General Regression Neural Networks Probabilistic Neural Networks regularization methods machine learning Šafr, Karel ISIS:79571 ths Zimmermann, Pavel, 1980- jo20251268570 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/91651/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/91651 Hlavní práce https://insis.vse.cz/zp/91651/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/91651/posudek/oponent/88346 Oponentura https://insis.vse.cz/zp/91651/priloha/32270 Přiloha k práci https://insis.vse.cz/zp/91651/podrobnosti dc:identifier NEPOSILAT VSKP vse91651 250819 91651