Training Set Approximation for Kernel Methods
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Training Set Approximation for Kernel Methods
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA102%2F03%2F0440" target="_blank" >GA102/03/0440: Recognizing human activities for automated video surveillance</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2003
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
6
Pages from-to
121-126
Publisher name
Czech Pattern Recognition Society
Place of publication
Prague
Event location
Valtice
Event date
Feb 3, 2003
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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