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Theoretical Issues in Neural Networks - Approximation - NAIL026

Title:	Teoretické otázky neuronových sítí - aproximace
Guaranteed by:	Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2022
Semester:	winter
E-Credits:	3
Hours per week, examination:	winter s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	cancelled
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	Mgr. Roman Neruda, CSc.
Class:	Informatika Mgr. - volitelný
Classification:	Informatics > Theoretical Computer Science
Pre-requisite :	NAIL002

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: G_I (31.10.2001)

The universal approximation property will be studied for different architectures of neural networks (multilayer perceptron, RBF networks, Gaussian bars). Functional equivalence and similar properties will be studied with consequences for genetic learning of neural networks.

Aim of the course - Czech

Last update: T_KTI (26.05.2008)

Na přednášce bude vyšetřována vlastnost univerzální aproximace na různých

architekturách NS

Literature - Czech

Last update: Mgr. Roman Neruda, CSc. (02.05.2006)

Šíma, J, Neruda R: Teoretické otázky neuronových sítí, Matfyzpress, 1997.

Syllabus -

Last update: G_I (17.05.2004)

The universal approximation property will be studied for different

architectures of neural networks (multilayer perceptron, RBF networks,

Gaussian bars). Functional equivalence and similar properties will be

studied with consequences for genetic learning of neural networks.