Extraction of Binary Features by Probabilistic Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F08%3A00311211" target="_blank" >RIV/67985556:_____/08:00311211 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/08:00031161
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Extraction of Binary Features by Probabilistic Neural Networks
Original language description
In order to design probabilistic neural networks in the framework of pattern recognition we estimate class-conditional probability distributions in the form of finite mixtures of product components. As the mixture components correspond to neurons we specify the properties of neurons in terms of component parameters. The probabilistic features defined by neuron outputs can be used to transform the classification problem without information loss and, simultaneously, the Shannon entropy of the feature space is minimized. We show that, instead of dimensionality reduction, the decision problem can be simplified by using binary approximation of the probabilistic features. In experiments the resulting binary features improve recognition accuracy but also theyare nearly independent - in accordance with the minimum entropy property.
Czech name
Extrakce binárních příznaků pomocí pravděpodobnostních neuronových sítí
Czech description
Extrakce binárních příznaků pomocí pravděpodobnostních neuronových sítí

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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