This HTML5 document contains 38 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n9http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n19http://localhost/temp/predkladatel/
n6http://purl.org/net/nknouf/ns/bibtex#
n5http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n7http://linked.opendata.cz/ontology/domain/vavai/
n11https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
n16http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21340%2F06%3A00125818%21RIV11-MSM-21340___/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n12http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n20http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n18http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n4http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21340%2F06%3A00125818%21RIV11-MSM-21340___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product components. Unfortunately, the underlying mixtures are not uniquely identifiable and moreover, the estimated mixture parameters are starting-point dependent. For this reason we assume the latent class model only to define a set of elementary properties and the related statistical decision problem. In order to avoid the problem of unique identification of latent classes we propose a hierarchical ``bottom up'' cluster analysis based on unifying the latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion. The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product components. Unfortunately, the underlying mixtures are not uniquely identifiable and moreover, the estimated mixture parameters are starting-point dependent. For this reason we assume the latent class model only to define a set of elementary properties and the related statistical decision problem. In order to avoid the problem of unique identification of latent classes we propose a hierarchical ``bottom up'' cluster analysis based on unifying the latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion.
dcterms:title
Minimum Information Loss Cluster Analysis for Categorical Data Minimum Information Loss Cluster Analysis for Categorical Data
skos:prefLabel
Minimum Information Loss Cluster Analysis for Categorical Data Minimum Information Loss Cluster Analysis for Categorical Data
skos:notation
RIV/68407700:21340/06:00125818!RIV11-MSM-21340___
n3:aktivita
n18:V
n3:aktivity
V
n3:dodaniDat
n4:2011
n3:domaciTvurceVysledku
n5:1909614
n3:druhVysledku
n10:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
485911
n3:idVysledku
RIV/68407700:21340/06:00125818
n3:jazykVysledku
n17:eng
n3:klicovaSlova
cluster analysis; categorical data; EM algorithm
n3:klicoveSlovo
n12:cluster%20analysis n12:categorical%20data n12:EM%20algorithm
n3:kontrolniKodProRIV
[95DC161ED977]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Praha
n3:nazevZdroje
Doktorandské dny 2006
n3:obor
n15:BA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n4:2006
n3:tvurceVysledku
Hora, Jan
n3:typAkce
n9:EUR
n3:zahajeniAkce
2006-11-10+01:00
s:numberOfPages
12
n6:hasPublisher
Česká technika - nakladatelství ČVUT
n11:isbn
80-01-03554-9
n19:organizacniJednotka
21340