Knowledge Discovery in Dynamic Data Using 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%3AA1601DP8" target="_blank" >RIV/61988987:17310/15:A1601DP8 - 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
Knowledge Discovery in Dynamic Data Using Neural Networks
Original language description
This article aims at knowledge discovery in dynamic data via classification based on neural networks. In our experimental study we have used three different types of neural networks based on Hebb, Adaline and backpropagation training rules. Our goal wasto discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability ofrecognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neuralnetwork.
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
Information Science and Applications, Lecture Notes in Electrical Engineering
ISBN
978-3-662-46577-6
ISSN
1876-1100
e-ISSN
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Number of pages
8
Pages from-to
575-582
Publisher name
Springer Verlag
Place of publication
Berlin Heidelberg
Event location
Pattaya, Thailand
Event date
Feb 24, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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