Zpět na vyhledávání

Juraj Škandera

Master's thesis

Modern methods for density estimation for continuous data

Modern methods for density estimation for continuous data
Abstract:
Odhad hustoty je jedna z kľúčových úloh pri manipulácii a pozorovaní neoznačených dát. Jej hlavný cieľ je odhad funkcie hustoty pravdepodobnosti pozorovaných dát. Získané znalosti o funkcii hustoty pravdepodobnosti potom poskytujú základ pre úlohy ako zhlukovanie alebo detekcia odľahlých hodnôt. V tejto práci sa zaoberáme niektorými prístupmi k tejto úlohe a porovnávame ich výkonnosť.
Abstract:
Density estimation is one of the key tasks when handling and observing unsupervised data. Its main purpose is to estimate the probability density function underlying the observed data. Knowledge of probability density function then provides a basis for tasks such as clustering or outlier detection. In this thesis, we review some of the approaches to this task and compare their performance.
 

Keywords

Density Estimation Continuous Data Neural Networks Density Forest
 
Language used: English
Date on which the thesis was submitted / produced: 20. 7. 2020
Identifier: https://is.muni.cz/th/unpkb/

Thesis defence

  • Date of defence: 15. 9. 2020
  • Supervisor: doc. RNDr. Tomáš Brázdil, Ph.D.
  • Reader: Ing. Jana Hozzová, Ph.D.

Citation record

Cite this text

Full text of thesis

Contents of on-line thesis archive
Published in Theses:
  • světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatiky
Složka Reference to the local database directory of the institution

Masaryk University

Faculty of Informatics

Master programme / field:
Applied Informatics / Bioinformatics

Theses on a related topic

List of theses with an identical keyword.
  • No theses on a related topic available.