This HTML5 document contains 41 embedded RDF statements represented using HTML+Microdata notation.

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

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n17http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n15http://linked.opendata.cz/resource/domain/vavai/projekt/
n11http://linked.opendata.cz/ontology/domain/vavai/
n16http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n18http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F67985807%3A_____%2F02%3A06020147%21RIV%2F2003%2FAV0%2FA06003%2FN/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n8http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n13http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n7http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F02%3A06020147%21RIV%2F2003%2FAV0%2FA06003%2FN
rdf:type
skos:Concept n11:Vysledek
dcterms:description
We first present a brief survey of hardness results for training feedforward neural networks. These results are then completed by the proof that the simplest architecture containing only a single neuron that applies a sigmoidal activation function sigma:R-->[alpha,beta], satisfying certain natural axioms, e.g. the standard (logistic) sigmoid or saturated-linear function, to the weighted sum of $n$ inputs is hard to train. In particular, the problem of finding the weights of such a unit that minimize... We first present a brief survey of hardness results for training feedforward neural networks. These results are then completed by the proof that the simplest architecture containing only a single neuron that applies a sigmoidal activation function sigma:R-->[alpha,beta], satisfying certain natural axioms, e.g. the standard (logistic) sigmoid or saturated-linear function, to the weighted sum of $n$ inputs is hard to train. In particular, the problem of finding the weights of such a unit that minimize...
dcterms:title
Training a Single Sigmoidal Neuron is Hard. Training a Single Sigmoidal Neuron is Hard.
skos:prefLabel
Training a Single Sigmoidal Neuron is Hard. Training a Single Sigmoidal Neuron is Hard.
skos:notation
RIV/67985807:_____/02:06020147!RIV/2003/AV0/A06003/N
n4:strany
2709;2729
n4:aktivita
n9:Z n9:P
n4:aktivity
P(LN00A056), Z(AV0Z1030915)
n4:cisloPeriodika
N/A
n4:dodaniDat
n7:2003
n4:domaciTvurceVysledku
n17:3031314
n4:druhVysledku
n14:J
n4:duvernostUdaju
n8:S
n4:entitaPredkladatele
n18:predkladatel
n4:idSjednocenehoVysledku
667207
n4:idVysledku
RIV/67985807:_____/02:06020147
n4:jazykVysledku
n13:eng
n4:klicovaSlova
sigmoidal neuron; loading problem; NP-hardness
n4:klicoveSlovo
n5:NP-hardness n5:loading%20problem n5:sigmoidal%20neuron
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[56AA68048A7F]
n4:nazevZdroje
Neural Computation
n4:obor
n12:BA
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:pocetUcastnikuAkce
0
n4:pocetZahranicnichUcastnikuAkce
0
n4:projekt
n15:LN00A056
n4:rokUplatneniVysledku
n7:2002
n4:svazekPeriodika
14
n4:tvurceVysledku
Šíma, Jiří
n4:zamer
n16:AV0Z1030915
s:issn
0899-7667
s:numberOfPages
20