Learning Finite Automaton from Noisy Observations -- A Simple Instance of a Bidirectional Signal-to-symbol
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A00105693" target="_blank" >RIV/68407700:21230/04:00105693 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Learning Finite Automaton from Noisy Observations -- A Simple Instance of a Bidirectional Signal-to-symbol
Original language description
This report investigates the way how to learn the finite automaton model of the activity observed in real world. The related theory is reviewed, solution proposed and experiments conducted. Learning finite automaton is similar to learning a discrete Hidden Markov Model (HMM) using a variant of EM algorithm. We used J. Dupa{v c}'s discrete HMM Toolbox in Matlab. The experimental part of this work deals with learning HMM from a synthetic training set generated from a known model. This approach provides us with ground truth.
Czech name
—
Czech description
—
Classification
Type
O - Miscellaneous
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GA102%2F03%2F0440" target="_blank" >GA102/03/0440: Recognizing human activities for automated video surveillance</a><br>
Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů