2019
Machine Learning Using TIL
MENŠÍK, Marek; Marie DUŽÍ; Adam ALBERT; Vojtěch PATSCKA; Miroslav PAJR et. al.Basic information
Original name
Machine Learning Using TIL
Authors
MENŠÍK, Marek; Marie DUŽÍ; Adam ALBERT; Vojtěch PATSCKA and Miroslav PAJR
Edition
321. vyd. Lappeenranta, Finland, Frontiers in Artificial Intelligence and Applications, p. 344-362, 19 pp. 2019
Publisher
IOS Press
Other information
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
RIV identification code
RIV/47813059:19240/19:A0000684
Organization unit
Faculty of Philosophy and Science in Opava
ISBN
978-1-64368-044-6
ISSN
EID Scopus
2-s2.0-85082518352
Keywords in English
Generalization; Heuristics; Machine learning; Specialization; Transparent internsional logic; TIL; Hypothesis
Tags
Tags
International impact, Reviewed
Changed: 16/12/2020 13:41, Mgr. Kamil Matula, Ph.D.
Abstract
In the original language
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.