Boosting in probabilistic neural networks.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F02%3A16020102" target="_blank" >RIV/67985556:_____/02:16020102 - 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
Boosting in probabilistic neural networks.
Original language description
It has been verified in practical experiments that the classification performance can be improved by increasing the weights of misclassified training samples. We prove that in case of maximum-likelihood estimation the weighting of discrete data vectors is asymptotically equivalent to multiplication of the estimated distributions by a positive function. Consequently, the Bayesian decision-making can be made asymptotically invariant with respect to arbitrary weighting of data under certain conditions.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2002
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 16th International Conference on Pattern Recognition.
ISBN
0-7695-1699-8
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
136-139
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Québec City [CA]
Event date
Aug 11, 2002
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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