OU Portal
Log In
Welcome
Applicants
Z6_60GI02O0O8IDC0QEJUJ26TJDI4
Error:
Javascript is disabled in this browser. This page requires Javascript. Modify your browser's settings to allow Javascript to execute. See your browser's documentation for specific instructions.
{}
Close
Publikační činnost
Probíhá načítání, čekejte prosím...
publicationId :
tempRecordId :
actionDispatchIndex :
navigationBranch :
pageMode :
tabSelected :
isRivValid :
Record type:
kapitola v odborné knize (C)
Home Department:
Katedra informatiky a počítačů (31400)
Title:
Artificial Intelligence Algorithms for Classification and Pattern Recognition
Citace
JARUŠEK, R. a Kocian, V. Artificial Intelligence Algorithms for Classification and Pattern Recognition.
In:
Pattern Recognition and Classification in Time Series Data.
United States of America: IGI Global, 2017. s. 53-85. ISBN 9781522505655.
Subtitle
Publication year:
2017
Obor:
Informatika
Form of publication:
Tištená verze
ISBN code:
9781522505655
Book title in original language:
Pattern Recognition and Classification in Time Series Data
Title of the edition and volume number:
neuvedeno
Place of publishing:
United States of America
Publisher name:
IGI Global
Issue reference (issue number):
:
Published:
Author of the source document:
Number of pages:
33
Book page count:
220
Page from:
53
Page to:
85
Book print run:
EID:
Key words in English:
Artificial Neural Network, Boosting, Classifier, Diversity of Classifiers, Soft Computing
Annotation in original language:
Classification tasks can be solved using so-called classifiers. A classifier is a computer based agent which can perform a classification task. There are many computational algorithms that can be utilized for classification purposes. Classifiers can be broadly divided into two categories: rule-based classifiers and computational intelligence based classifiers usually called soft computing. Rule-based classifiers are generally constructed by the designer, where the designer defines rules for the interpretation of detected inputs. This is in contrast to soft-computing based classifiers, where the designer only creates a basic framework for the interpretation of data. The learning or training algorithms within such systems are responsible for the generation of rules for the correct interpretation of data.
Annotation in english language:
References
Reference
R01:
RIV/61988987:17310/17:A2301HLN
Complementary Content
Deferred Modules
${title}
${badge}
${loading}
Deferred Modules