Another Adaptive Approach to Novelty Detection in Time Series
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F14%3A00212030" target="_blank" >RIV/68407700:21220/14:00212030 - isvavai.cz</a>
Result on the web
<a href="http://airccj.org/2013/aisc14/acceptedpapers.html" target="_blank" >http://airccj.org/2013/aisc14/acceptedpapers.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5121/csit.2014.4229" target="_blank" >10.5121/csit.2014.4229</a>
Alternative languages
Result language
angličtina
Original language name
Another Adaptive Approach to Novelty Detection in Time Series
Original language description
This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is shown, as the modification of the recently introduced adaptive approach of novelty detection. Static and dynamic neural network models are shown on theoretical data as well as on a real ECG signal.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
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 Science & Information Technology
ISBN
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ISSN
2231-5403
e-ISSN
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Number of pages
11
Pages from-to
341-351
Publisher name
AIRCC Publishing Corporation
Place of publication
Chennai, Tamil Nadu
Event location
Sydney
Event date
Feb 21, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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