MENŠÍK, Marek, Marie DUŽÍ, Adam ALBERT, Vojtěch PATSCKA and Miroslav PAJR. Machine Learning Using TIL. Online. In Dhanayake, A.; Huiskonen, J.; Kiyoki, Y.; Thalheim, B.; Jaakkola, H.; Yoshida, N. Frontiers in Artificial Intelligence and Applications. 321st ed. Lappeenranta, Finland: IOS Press, 2019, p. 344-362. ISBN 978-1-64368-044-6. Available from: https://dx.doi.org/10.3233/FAIA200024.
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Basic information
Original name Machine Learning Using TIL
Authors MENŠÍK, Marek (203 Czech Republic, guarantor), Marie DUŽÍ (203 Czech Republic), Adam ALBERT (203 Czech Republic), Vojtěch PATSCKA (203 Czech Republic) and Miroslav PAJR (203 Czech Republic, belonging to the institution).
Edition 321. vyd. Lappeenranta, Finland, Frontiers in Artificial Intelligence and Applications, p. 344-362, 19 pp. 2019.
Publisher IOS Press
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Finland
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW Informační stránka o příspěvku u vydavatele sborníku
RIV identification code RIV/47813059:19240/19:A0000684
Organization unit Faculty of Philosophy and Science in Opava
ISBN 978-1-64368-044-6
ISSN 0922-6389
Doi http://dx.doi.org/10.3233/FAIA200024
Keywords in English Generalization; Heuristics; Machine learning; Specialization; Transparent internsional logic; TIL; Hypothesis
Tags ÚI
Tags International impact, Reviewed
Changed by Changed by: Mgr. Kamil Matula, Ph.D., učo 7389. Changed: 16/12/2020 13:41.
Abstract
In this paper we deal with machine learning methods and algorithms applied to the area of geographic data. First, we briefly introduce learning with a supervisor that is applied in our case. Then we describe the algorithm ‘Framework’ together with heuristic methods used in it. Definitions of particular geographic objects, i.e. their concepts, are formulated in our background theory Transparent Intensional Logic (TIL) as TIL constructions. These concepts serve as general hypotheses. Basic principles of supervised machine learning are generalization and specialization. Given a positive example, the learner generalizes, while after a near-miss example specialization is applied. Heuristic methods deal with the way generalization and specialization are applied.
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