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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F07%3A00090278%21RIV08-AV0-67985556
rdf:type
n9:Vysledek skos:Concept
dcterms:description
We summarize the main results on probabilistic neural networks recently published in a series of papers. Considering the framework of statistical pattern recognition we assume approximation of class-conditional distributions by finite mixtures of product components. The probabilistic neurons correspond to mixture components and can be interpreted in neurophysiological terms. In this way we can find possible theoretical background of the functional properties of neurons. For example, the general formula for synaptical weights provides a statistical justification of the well known Hebbian principle of learning. Similarly, the mean effect of lateral inhibition can be expressed by means of a formula proposed by Perez as a measure of dependence tightness of involved variables. Souhrnná práce o pravděpodobnostních neuronových sítích, které nabízejí alternativní řešení problému výběru příznaků (podprostorový přístup) a jsou široce použitelné pro řešení mnohorozměrných úloh klasifikace s omezenými datovými soubory. We summarize the main results on probabilistic neural networks recently published in a series of papers. Considering the framework of statistical pattern recognition we assume approximation of class-conditional distributions by finite mixtures of product components. The probabilistic neurons correspond to mixture components and can be interpreted in neurophysiological terms. In this way we can find possible theoretical background of the functional properties of neurons. For example, the general formula for synaptical weights provides a statistical justification of the well known Hebbian principle of learning. Similarly, the mean effect of lateral inhibition can be expressed by means of a formula proposed by Perez as a measure of dependence tightness of involved variables.
dcterms:title
Neuromorphic features of probabilistic neural networks Neuromorphic features of probabilistic neural networks Neuromorfní vlastnosti pravděpodobnostních neuronových sítí
skos:prefLabel
Neuromorfní vlastnosti pravděpodobnostních neuronových sítí Neuromorphic features of probabilistic neural networks Neuromorphic features of probabilistic neural networks
skos:notation
RIV/67985556:_____/07:00090278!RIV08-AV0-67985556
n3:strany
697;712
n3:aktivita
n8:R n8:P n8:Z
n3:aktivity
P(1M0572), P(GA102/07/1594), R, Z(AV0Z10750506)
n3:cisloPeriodika
5
n3:dodaniDat
n11:2008
n3:domaciTvurceVysledku
n15:5728525
n3:druhVysledku
n17:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
437019
n3:idVysledku
RIV/67985556:_____/07:00090278
n3:jazykVysledku
n18:eng
n3:klicovaSlova
probabilistic neural networks; distribution mixtures; sequential EM algorithm; pattern recognition
n3:klicoveSlovo
n4:probabilistic%20neural%20networks n4:sequential%20EM%20algorithm n4:pattern%20recognition n4:distribution%20mixtures
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[D7DC30FEF6AF]
n3:nazevZdroje
Kybernetika
n3:obor
n14:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n7:GA102%2F07%2F1594 n7:1M0572
n3:rokUplatneniVysledku
n11:2007
n3:svazekPeriodika
43
n3:tvurceVysledku
Grim, Jiří
n3:zamer
n16:AV0Z10750506
s:issn
0023-5954
s:numberOfPages
16