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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F07%3A00021213%21RIV11-AV0-27240___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
An information retrieval (IR) system (IRs) (search engine) is said to be efficient, to the degree that always evaluates each object in the information base (database, document base, web,...) like the expert. The ability of IRs's is to retrieve mostly all relevant objects (measured by the recall), and only the (most) relevant objects (measured by the precision) from the collection queried. Recall and precision measures provide the classical measure of the retrieval efficiency. They measure the degree to which the query answer (the set of documents that retrieved by IRs as response to the user query). Where, the query answer is the set of relevant documents in the information based queried. Retrieving most relevant documents to the user query in IRs was one of the most important methods of World Wide Web (WWW) search engines used in the world now. An information retrieval (IR) system (IRs) (search engine) is said to be efficient, to the degree that always evaluates each object in the information base (database, document base, web,...) like the expert. The ability of IRs's is to retrieve mostly all relevant objects (measured by the recall), and only the (most) relevant objects (measured by the precision) from the collection queried. Recall and precision measures provide the classical measure of the retrieval efficiency. They measure the degree to which the query answer (the set of documents that retrieved by IRs as response to the user query). Where, the query answer is the set of relevant documents in the information based queried. Retrieving most relevant documents to the user query in IRs was one of the most important methods of World Wide Web (WWW) search engines used in the world now.
dcterms:title
Grow up precision recall relationship curve in IR system using GP and fuzzy optimization in optimizing the user query Grow up precision recall relationship curve in IR system using GP and fuzzy optimization in optimizing the user query
skos:prefLabel
Grow up precision recall relationship curve in IR system using GP and fuzzy optimization in optimizing the user query Grow up precision recall relationship curve in IR system using GP and fuzzy optimization in optimizing the user query
skos:notation
RIV/61989100:27240/07:00021213!RIV11-AV0-27240___
n4:aktivita
n12:Z n12:P
n4:aktivity
P(1ET100300414), Z(MSM6198910027)
n4:cisloPeriodika
4
n4:dodaniDat
n5:2011
n4:domaciTvurceVysledku
n6:4347269 Owais, Suhail Sami Jebour n6:9175970
n4:druhVysledku
n13:J
n4:duvernostUdaju
n10:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
423682
n4:idVysledku
RIV/61989100:27240/07:00021213
n4:jazykVysledku
n16:eng
n4:klicovaSlova
recall and harmonic mean; precision; term weights; Boolean operator; fuzzy optimization; genetic programming; information retrieval
n4:klicoveSlovo
n7:fuzzy%20optimization n7:recall%20and%20harmonic%20mean n7:term%20weights n7:precision n7:Boolean%20operator n7:genetic%20programming n7:information%20retrieval
n4:kodStatuVydavatele
CZ - Česká republika
n4:kontrolniKodProRIV
[E144B5F93E80]
n4:nazevZdroje
NEURAL NETWORK WORLD
n4:obor
n11:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n15:1ET100300414
n4:rokUplatneniVysledku
n5:2007
n4:svazekPeriodika
17
n4:tvurceVysledku
Krömer, Pavel Owais, Suhail Sami Jebour Snášel, Václav
n4:wos
000249076100004
n4:zamer
n18:MSM6198910027
s:issn
1210-0552
s:numberOfPages
15
n19:organizacniJednotka
27240