quantitykind:InformationEntropy

URI: http://qudt.org/vocab/quantitykind/InformationEntropy

Type
Description

Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning.

Properties
qudt:plainTextDescription
Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning.
Annotations
rdfs:comment
Applicable units are those of quantitykind:InformationEntropy
dcterms:description
Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning.
rdfs:label
Information Entropy(en)
View as:  CSV

Work in progress

RDF/XML
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    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
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    <j.1:description rdf:datatype="http://qudt.org/schema/qudt/LatexString">Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning.</j.1:description>
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    <j.0:plainTextDescription>Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning.</j.0:plainTextDescription>
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    <j.0:applicableUnit rdf:resource="http://qudt.org/vocab/unit/GibiBYTE"/>
    <rdfs:label xml:lang="en">Information Entropy</rdfs:label>
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TURTLE
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  <http://qudt.org/schema/qudt/hasDimensionVector> <http://qudt.org/vocab/dimensionvector/A0E0L0I0M0H0T0D1> ;
  <http://qudt.org/schema/qudt/informativeReference> "http://simple.wikipedia.org/wiki/Information_entropy"^^xsd:anyURI ;
  <http://qudt.org/schema/qudt/plainTextDescription> "Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning." ;
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  rdfs:label "Information Entropy"@en ;
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.
JSON
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    ,"applicable unit":"unit:ExbiBYTE" 
    ,"applicable unit":"unit:GibiBYTE" 
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    ,"applicable unit":"unit:SHANNON" 
    ,"applicable unit":"unit:TebiBYTE" 
    ,"applicable unit":"unit:TeraBYTE" 
    ,"comment":"Applicable units are those of quantitykind:InformationEntropy" 
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    ,"isDefinedBy":"&lt;http:\/\/qudt.org\/2.1\/vocab\/quantitykind&gt;" 
    ,"label":"Information Entropy" 
    ,"type":"qudt:QuantityKind" 
    ]}
JSON-LD
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  "description" : "Information Entropy is a concept from information theory. It tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain. The concept of information entropy was created by a mathematician. He was named Claude Elwood Shannon. It has applications in many areas, including lossless data compression, statistical inference, cryptography and recently in other disciplines as biology, physics or machine learning.",
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