Utilization of Singularity Exponent in Nearest Neighbor Based Classifier

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00375796" target="_blank" >RIV/67985807:_____/13:00375796 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/13:00214975

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Utilization of Singularity Exponent in Nearest Neighbor Based Classifier

Original language description

Classifiers serve as tools for classifying data into classes. They directly or indirectly take a distribution of data points around a given query point into account. To express the distribution of points from the viewpoint of distances from a given point, a probability distribution mapping function is introduced here. The approximation of this function in a form of a suitable power of the distance is presented. How to state this power - the distribution mapping exponent -- is described. This exponent isused for probability density estimation in high-dimensional spaces and for classification. A close relation of the exponent to a singularity exponent is discussed. It is also shown that this classifier exhibits significantly better behavior (classification accuracy) than other kinds of classifiers for some tasks.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2013

Confidentiality

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

Name of the periodical

Journal of Classification

ISSN

0176-4268

e-ISSN

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Volume of the periodical

30

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

27

Pages from-to

3-29

UT code for WoS article

000315441300002

EID of the result in the Scopus database

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