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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

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

  • e-ISSN

  • 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