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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F06%3A03124623" target="_blank" >RIV/68407700:21230/06:03124623 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Greedy Kernel Principal Component Analysis

Popis výsledku v původním jazyce

This contribution discusses one aspect of statistical learning and generalization. Theory of learning is very relevant to cognitive systems including cognitive vision. A technique allowing to approximate a huge training set is proposed. The approach aimsto represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. As the computations inthe proposed algorithm appear in a form of dot product, kernel methods can be used to cope with non-linear problems. The proposed method was tested to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discr

Název v anglickém jazyce

Greedy Kernel Principal Component Analysis

Popis výsledku anglicky

This contribution discusses one aspect of statistical learning and generalization. Theory of learning is very relevant to cognitive systems including cognitive vision. A technique allowing to approximate a huge training set is proposed. The approach aimsto represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. As the computations inthe proposed algorithm appear in a form of dot product, kernel methods can be used to cope with non-linear problems. The proposed method was tested to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discr

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA102%2F03%2F0440" target="_blank" >GA102/03/0440: Rozpoznávání lidských aktivit pro automatické sledování z videa</a><br>

Návaznosti

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

Rok uplatnění

2006

Kód důvěrnosti údajů

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

Název statě ve sborníku

Cognitive Vision Systems

ISBN

3-540-33971-X

ISSN

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e-ISSN

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Počet stran výsledku

20

Strana od-do

87-106

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Dagstuhl Castle

Datum konání akce

26. 10. 2003

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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