Learning Entropy: Multiscale Measure for Incremental Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F13%3A00212013" target="_blank" >RIV/68407700:21220/13:00212013 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.mdpi.com/1099-4300/15/10/4159" target="_blank" >http://www.mdpi.com/1099-4300/15/10/4159</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e15104159" target="_blank" >10.3390/e15104159</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Entropy: Multiscale Measure for Incremental Learning

Popis výsledku v původním jazyce

First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishes anovel non-Shannon and non-probabilistic concept of novelty quantification, i.e., Entropy of Learning, or in short the Learning Entropy. This novel cognitive measure can be used for evaluation of each newly measured sample of data, or even of whole intervals. The Learning Entropy is quantified in respect to the inconsistency of data to the temporary governing law of system behavior that is incrementally learned by adaptive models such as linear or polynomial adaptive filters or neural networks. The paperpresents this novel concept on the example of gradient descent learning technique with normalized learning rate.

Název v anglickém jazyce

Learning Entropy: Multiscale Measure for Incremental Learning

Popis výsledku anglicky

First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishes anovel non-Shannon and non-probabilistic concept of novelty quantification, i.e., Entropy of Learning, or in short the Learning Entropy. This novel cognitive measure can be used for evaluation of each newly measured sample of data, or even of whole intervals. The Learning Entropy is quantified in respect to the inconsistency of data to the temporary governing law of system behavior that is incrementally learned by adaptive models such as linear or polynomial adaptive filters or neural networks. The paperpresents this novel concept on the example of gradient descent learning technique with normalized learning rate.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/TA03020312" target="_blank" >TA03020312: Výzkum oxyfuel spalování ve stacionární fluidní vrstvě pro CCS technologie</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Entropy

ISSN

1099-4300

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

29

Strana od-do

4159-4187

Kód UT WoS článku

000328486900010

EID výsledku v databázi Scopus

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