About: Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy

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About: Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889834
Description	This paper presents recently introduced concept of Learning Entropy (LE) for time series and recalls the practical form of its evaluation in real time. Then, a technique that estimates the increased risk of prediction inaccuracy of adaptive predictors in real time using LE is introduced. On simulation examples using artificial signal and real respiratory time series, it is shown that LE can be used to evaluate the actual validity of the adaptive predicting model of time series in real time. The introduced technique is discussed as a potential approach to the improvement of accuracy of lung tumor tracking radiation therapy. This paper presents recently introduced concept of Learning Entropy (LE) for time series and recalls the practical form of its evaluation in real time. Then, a technique that estimates the increased risk of prediction inaccuracy of adaptive predictors in real time using LE is introduced. On simulation examples using artificial signal and real respiratory time series, it is shown that LE can be used to evaluate the actual validity of the adaptive predicting model of time series in real time. The introduced technique is discussed as a potential approach to the improvement of accuracy of lung tumor tracking radiation therapy. (en)
Title	Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy (en)
skos:prefLabel	Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy (en)
skos:notation	RIV/68407700:21220/14:00224858!RIV15-MSM-21220___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Cejnek, Matouš Bukovský, Ivo
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta strojní
http://linked.open...dnocenehoVysledku	48294
http://linked.open...ai/riv/idVysledku	RIV/68407700:21220/14:00224858
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Novelty Detection; lung tumor motion; prediction; target tracking; radiation therapy; neural networks (en)
http://linked.open.../riv/klicoveSlovo	Novelty Detection neural networks prediction radiation therapy target tracking lung tumor motion
http://linked.open...ontrolniKodProRIV	[DAFC0D745E5A]
http://linked.open...v/mistoKonaniAkce	Beijing
http://linked.open...i/riv/mistoVydani	Piscataway
http://linked.open...i/riv/nazevZdroje	Neural Networks (IJCNN), 2014 International Joint Conference on - Scopus ISBN
http://linked.open...in/vavai/riv/obor	BC
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Bukovský, Ivo Homma, N. Kei, I. Cejnek, Matouš
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-07-06 (xsd:date)
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/IJCNN.2014.6889834
http://purl.org/ne...btex#hasPublisher	IEEE
https://schema.org/isbn	978-1-4799-1484-5
http://localhost/t...ganizacniJednotka	21220

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