Applying a Reference Objects Preselection Algorithm to Real-World Data

The problem of optimal selection of learning objects is investigated. The effectiveness of the previously proposed iterative method for generating sets of relevant precedents is demonstrated on real-world data.

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Veröffentlicht in:Pattern recognition and image analysis Jg. 28; H. 3; S. 427 - 429
1. Verfasser: Bondarenko, N. N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.07.2018
Springer Nature B.V
Schlagworte:
Applied Problems
Computer Science
Image Processing and Computer Vision
Machine learning
Pattern Recognition
kestimate calculation algorithms
algebraic theory of algorithms
set of relevant precedents
selection of learning objects
and brain–computer interface
ISSN:1054-6618, 1555-6212
Online-Zugang:Volltext
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Zusammenfassung:The problem of optimal selection of learning objects is investigated. The effectiveness of the previously proposed iterative method for generating sets of relevant precedents is demonstrated on real-world data.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661818030045