Pattern Matching in Sequential Data Using Reservoir Projections
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10253426" target="_blank" >RIV/61989100:27240/19:10253426 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-22796-8_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-22796-8_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-22796-8_19" target="_blank" >10.1007/978-3-030-22796-8_19</a>
Alternative languages
Result language
angličtina
Original language name
Pattern Matching in Sequential Data Using Reservoir Projections
Original language description
A relevant problem on data science is to define an efficient and reliable algorithm for finding specific patterns in a given signal. This type of problems often appears in medical applications, biophysical systems, complex systems, financial analysis, and several other domains. Here, we introduce a new model based in the ability of Recurrent Neural Networks (RNNs) for modelling time series. The technique encodes temporal information of the reference signal and the given query in a feature space. This encoding is done using a RNN. In the feature space, we apply similarity techniques for analysing differences among the projected points. The proposed method presents advantages with respect of state of art, it can produce good results using less computational costs. We discuss the proposal over three benchmark datasets. (C) 2019, Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TN01000024" target="_blank" >TN01000024: National Competence Center - Cybernetics and Artificial Intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 11554
ISBN
978-3-030-22795-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
173-183
Publisher name
Springer
Place of publication
Cham
Event location
Moskva
Event date
Jul 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000611781800019