Learning Automata Using Dimensional Reduction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10456345" target="_blank" >RIV/00216208:11320/22:10456345 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-22419-5_4" target="_blank" >https://doi.org/10.1007/978-3-031-22419-5_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-22419-5_4" target="_blank" >10.1007/978-3-031-22419-5_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Automata Using Dimensional Reduction

Popis výsledku v původním jazyce

One-dimensional (string) formal languages and their learning have been studied in considerable depth. However, the knowledge of their two-dimensional (picture) counterpart, which retains similar importance, is lacking. We investigate the problem of learning formal two-dimensional picture languages by applying learning methods for one-dimensional (string) languages. We formalize the transcription process from an input two-dimensional picture into a string and propose a few adaptations to it. These proposals are then tested in a series of experiments, and their outcomes are compared. Finally, these methods are applied to a practical problem and learn an automaton for recognizing a part of the MNIST dataset. The obtained results show improvements in the topic and the potential in using the learning of automata in fitting problems.

Název v anglickém jazyce

Learning Automata Using Dimensional Reduction

Popis výsledku anglicky

One-dimensional (string) formal languages and their learning have been studied in considerable depth. However, the knowledge of their two-dimensional (picture) counterpart, which retains similar importance, is lacking. We investigate the problem of learning formal two-dimensional picture languages by applying learning methods for one-dimensional (string) languages. We formalize the transcription process from an input two-dimensional picture into a string and propose a few adaptations to it. These proposals are then tested in a series of experiments, and their outcomes are compared. Finally, these methods are applied to a practical problem and learn an automaton for recognizing a part of the MNIST dataset. The obtained results show improvements in the topic and the potential in using the learning of automata in fitting problems.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Artificial Intelligence - {IBERAMIA} 2022 - 17th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 23-25, 2022, Proceedings

ISBN

978-3-031-22418-8

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

41-52

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Cartagena de Indias, Colombia

Datum konání akce

23. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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