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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F14%3A10287863%21RIV15-GA0-11320___
rdf:type
skos:Concept n19:Vysledek
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-319-11179-7_64#page-1
dcterms:description
Sensitivity analysis allows to assess the influence of each neuron or weight on the final network output. This capability is crucial for various feature selection and pruning strategies. In this paper, we present a new approximative sensitivity-based training algorithm yielding robust neural networks with generalization capabilities comparable to its exact analytical counterpart, yet much faster. Sensitivity analysis allows to assess the influence of each neuron or weight on the final network output. This capability is crucial for various feature selection and pruning strategies. In this paper, we present a new approximative sensitivity-based training algorithm yielding robust neural networks with generalization capabilities comparable to its exact analytical counterpart, yet much faster.
dcterms:title
Fast Sensitivity-Based Training of BP-Networks Fast Sensitivity-Based Training of BP-Networks
skos:prefLabel
Fast Sensitivity-Based Training of BP-Networks Fast Sensitivity-Based Training of BP-Networks
skos:notation
RIV/00216208:11320/14:10287863!RIV15-GA0-11320___
n3:aktivita
n8:P
n3:aktivity
P(GAP103/10/0783)
n3:dodaniDat
n5:2015
n3:domaciTvurceVysledku
n4:6605850 n4:6046797
n3:druhVysledku
n16:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n22:predkladatel
n3:idSjednocenehoVysledku
16514
n3:idVysledku
RIV/00216208:11320/14:10287863
n3:jazykVysledku
n17:eng
n3:klicovaSlova
generalization; pruning; feature selection; sensitivity analysis; internal representation; back-propagation; neural networks
n3:klicoveSlovo
n7:internal%20representation n7:pruning n7:back-propagation n7:feature%20selection n7:generalization n7:sensitivity%20analysis n7:neural%20networks
n3:kontrolniKodProRIV
[8BA03D083CD4]
n3:mistoKonaniAkce
Hamburg, Germany
n3:mistoVydani
Berlin
n3:nazevZdroje
Artificial Neural Networks and Machine Learning - ICANN 2014
n3:obor
n12:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n18:GAP103%2F10%2F0783
n3:rokUplatneniVysledku
n5:2014
n3:tvurceVysledku
Petříčková, Zuzana Mrázová, Iveta
n3:typAkce
n21:WRD
n3:zahajeniAkce
2014-09-15+02:00
s:issn
0302-9743
s:numberOfPages
8
n13:hasPublisher
Springer-Verlag
n15:isbn
978-3-319-11178-0
n10:organizacniJednotka
11320