Sequential pattern recognition by maximum conditional informativity

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F14%3A00428565" target="_blank" >RIV/67985556:_____/14:00428565 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.patrec.2014.02.024" target="_blank" >http://dx.doi.org/10.1016/j.patrec.2014.02.024</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.patrec.2014.02.024" target="_blank" >10.1016/j.patrec.2014.02.024</a>

Result language
angličtina
Original language name
Sequential pattern recognition by maximum conditional informativity
Original language description
Sequential pattern recognition assumes the features to be measured successively, one at a time, and therefore the key problem is to choose the next feature optimally. However, the choice of the features may be strongly influenced by the previous featuremeasurements and therefore the on-line ordering of features is difficult. There are numerous methods to estimate class-conditional probability distributions but it is usually computationally intractable to derive the corresponding conditional marginals.In literature there is no exact method of on-line feature ordering except for the strongly simplifying naive Bayes models. We show that the problem of sequential recognition has an explicit analytical solution which is based on approximation of the class-conditional distributions by mixtures of product components.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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