Input Filters Implementing Diversity in Ensemble of Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F15%3AA1601EAV" target="_blank" >RIV/61988987:17310/15:A1601EAV - 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
Input Filters Implementing Diversity in Ensemble of Neural Networks
Original language description
This paper discusses possibilities how to use input filters to improve performance in ensemble of neural-networks-based classifiers. The proposed method is based on filtering of input vectors in the used training set, which minimize demands on data preprocessing. Our approach comes out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. In the experimental study, we verified that such classifiers are able to sufficiently classify the submitted data into predefined classes without knowledge of details of their significance.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science
ISBN
978-3-319-19643-5
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
307-318
Publisher name
Springer International Publishing
Place of publication
Switzerland
Event location
Bilbao, Spain
Event date
Jun 22, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000363689900026