Training Set Approximation for Kernel Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F03%3A03087039" target="_blank" >RIV/68407700:21230/03:03087039 - 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

Training Set Approximation for Kernel Methods

Popis výsledku v původním jazyce

A technique for a training set approximation and its usage in kernel methods is proposed. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis. In contrast to the PCA, the basis vectors of the low dimensional space used for data approximation 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 lowcomputational requirements and allows on-line processing of huge data sets. The proposed method was used to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discriminant which are known method for learning classifiers. The experiments show that the proposed approximation can significantly reduce the complexity of the found classifiers while retaining their accuracy.

Název v anglickém jazyce

Training Set Approximation for Kernel Methods

Popis výsledku anglicky

A technique for a training set approximation and its usage in kernel methods is proposed. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis. In contrast to the PCA, the basis vectors of the low dimensional space used for data approximation 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 lowcomputational requirements and allows on-line processing of huge data sets. The proposed method was used to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discriminant which are known method for learning classifiers. The experiments show that the proposed approximation can significantly reduce the complexity of the found classifiers while retaining their accuracy.

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

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2003

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computer Vision - CVWW'03 : Proceedings of the 8th Computer Vision Winter Workshop

ISBN

80-238-9967-8

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

121-126

Název nakladatele

Czech Pattern Recognition Society

Místo vydání

Prague

Místo konání akce

Valtice

Datum konání akce

3. 2. 2003

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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