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Course, academic year 2019/2020
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Theoretical Issues in Neural Networks - Approximation - NAIL026
Title in English: 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 2015
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: Czech
Teaching methods: full-time
Guarantor: Mgr. Roman Neruda, CSc.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Theoretical Computer Science
Pre-requisite : NAIL002
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.

 
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