Artificial Intelligence Algorithms for Classification and Pattern Recognition
Result description
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.
Keywords
Artificial Neural NetworkBoostingClassifierDiversity of ClassifiersSoft Computing
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/61988987:17310/17:A1701HLN
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Artificial Intelligence Algorithms for Classification and Pattern Recognition
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Pattern Recognition and Classification in Time Series Data
ISBN
9781522505655
Number of pages of the result
33
Pages from-to
53-85
Number of pages of the book
220
Publisher name
IGI Global
Place of publication
United States of America
UT code for WoS chapter
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Basic information
Result type
C - Chapter in a specialist book
OECD FORD
Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Year of implementation
2017