Sequential pattern recognition by maximum conditional informativity

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential pattern recognition by maximum conditional informativity

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Sequential pattern recognition by maximum conditional informativity

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pattern Recognition Letters

ISSN

0167-8655

e-ISSN

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Svazek periodika

45

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

39-45

Kód UT WoS článku

000337219200006

EID výsledku v databázi Scopus

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