Pattern Matching in Sequential Data Using Reservoir Projections

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Pattern Matching in Sequential Data Using Reservoir Projections

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Pattern Matching in Sequential Data Using Reservoir Projections

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TN01000024" target="_blank" >TN01000024: Národní centrum kompetence - Kybernetika a umělá inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

11

Strana od-do

173-183

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Moskva

Datum konání akce

10. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000611781800019