FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2969031145> ?p ?o ?g. }

• W2969031145 endingPage "116452" @default.
• W2969031145 startingPage "116438" @default.
• W2969031145 abstract "The Generalized Adaptive Resonance Theory (GART) model is a supervised online learning neural network based on an integration of Adaptive Resonance Theory (ART) and the Generalized Regression Neural Network (GRNN). It is capable of online learning, and is suitable for undertaking both classification and regression problems. In this paper, we further enhance GART (EGART) with four improvements to formulate a new EGART model. Three operating strategies for the EGART model to undertake regression problems are suggested. The first operating strategy is a fully online learning EGART model. The second operating strategy involves Ordering Algorithm for determining the presentation sequence of training samples during the initial training of EGART model. This strategy is considered as offline learning because a set of data samples must be available for the Ordering Algorithm to compute the best presentation sequence (hereinafter denoted as Ordered-EGART). The third operating strategy aims to demonstrate online learning capability of EART model (the first operating strategy) can still be resumed after training on the Ordered-EGART. It is most suitable for applications with a set of ready data samples and their sequences are predetermined by Ordering Algorithm prior to training of EGART model in offline mode, and triggers online learning when more new data samples become available (hereinafter denoted as IO-EGART). A series of experiments with five benchmark data sets from various application domains is conducted to assess and compare the effectiveness of the EGART model and three operating strategies with those of other methods published in literature as well as two fire safety engineering problems, i.e., predicting the thermal interface height in a single compartment fire and evacuation times in the event of fire. The results and comparisons with other approaches positively demonstrate the efficacy and applicability of EGART model as a useful data regression model for tackling fire safety engineering problems." @default.
• W2969031145 created "2019-08-22" @default.
• W2969031145 creator A5062531171 @default.
• W2969031145 creator A5072923302 @default.
• W2969031145 creator A5090768303 @default.
• W2969031145 creator A5091148707 @default.
• W2969031145 date "2019-01-01" @default.
• W2969031145 modified "2023-10-18" @default.
• W2969031145 title "Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network" @default.
• W2969031145 cites W1554663460 @default.
• W2969031145 cites W1653626617 @default.
• W2969031145 cites W1965198822 @default.
• W2969031145 cites W1982610692 @default.
• W2969031145 cites W1990938413 @default.
• W2969031145 cites W2012611887 @default.
• W2969031145 cites W2042917546 @default.
• W2969031145 cites W2062223829 @default.
• W2969031145 cites W2074349839 @default.
• W2969031145 cites W2080531009 @default.
• W2969031145 cites W2092108829 @default.
• W2969031145 cites W2099244771 @default.
• W2969031145 cites W2104344166 @default.
• W2969031145 cites W2107170260 @default.
• W2969031145 cites W2110158084 @default.
• W2969031145 cites W2111072639 @default.
• W2969031145 cites W2114865067 @default.
• W2969031145 cites W2116119284 @default.
• W2969031145 cites W2131246644 @default.
• W2969031145 cites W2131830135 @default.
• W2969031145 cites W2135899410 @default.
• W2969031145 cites W2141695047 @default.
• W2969031145 cites W2145645453 @default.
• W2969031145 cites W2149723649 @default.
• W2969031145 cites W2155399784 @default.
• W2969031145 cites W2158054309 @default.
• W2969031145 cites W2165199752 @default.
• W2969031145 cites W2166280719 @default.
• W2969031145 cites W2321627895 @default.
• W2969031145 cites W2519420704 @default.
• W2969031145 cites W2595141258 @default.
• W2969031145 cites W2617137613 @default.
• W2969031145 cites W2765370858 @default.
• W2969031145 cites W2765452429 @default.
• W2969031145 cites W2790121727 @default.
• W2969031145 cites W2804196292 @default.
• W2969031145 cites W2838248257 @default.
• W2969031145 cites W2889441897 @default.
• W2969031145 cites W2946983027 @default.
• W2969031145 doi "https://doi.org/10.1109/access.2019.2935454" @default.
• W2969031145 hasPublicationYear "2019" @default.
• W2969031145 type Work @default.
• W2969031145 sameAs 2969031145 @default.
• W2969031145 citedByCount "2" @default.
• W2969031145 countsByYear W29690311452020 @default.
• W2969031145 countsByYear W29690311452022 @default.
• W2969031145 crossrefType "journal-article" @default.
• W2969031145 hasAuthorship W2969031145A5062531171 @default.
• W2969031145 hasAuthorship W2969031145A5072923302 @default.
• W2969031145 hasAuthorship W2969031145A5090768303 @default.
• W2969031145 hasAuthorship W2969031145A5091148707 @default.
• W2969031145 hasBestOaLocation W29690311451 @default.
• W2969031145 hasConcept C105795698 @default.
• W2969031145 hasConcept C115755159 @default.
• W2969031145 hasConcept C119857082 @default.
• W2969031145 hasConcept C124101348 @default.
• W2969031145 hasConcept C13280743 @default.
• W2969031145 hasConcept C152877465 @default.
• W2969031145 hasConcept C154945302 @default.
• W2969031145 hasConcept C177264268 @default.
• W2969031145 hasConcept C185798385 @default.
• W2969031145 hasConcept C199360897 @default.
• W2969031145 hasConcept C205649164 @default.
• W2969031145 hasConcept C2777851325 @default.
• W2969031145 hasConcept C33923547 @default.
• W2969031145 hasConcept C41008148 @default.
• W2969031145 hasConcept C50644808 @default.
• W2969031145 hasConcept C58489278 @default.
• W2969031145 hasConcept C83546350 @default.
• W2969031145 hasConceptScore W2969031145C105795698 @default.
• W2969031145 hasConceptScore W2969031145C115755159 @default.
• W2969031145 hasConceptScore W2969031145C119857082 @default.
• W2969031145 hasConceptScore W2969031145C124101348 @default.
• W2969031145 hasConceptScore W2969031145C13280743 @default.
• W2969031145 hasConceptScore W2969031145C152877465 @default.
• W2969031145 hasConceptScore W2969031145C154945302 @default.
• W2969031145 hasConceptScore W2969031145C177264268 @default.
• W2969031145 hasConceptScore W2969031145C185798385 @default.
• W2969031145 hasConceptScore W2969031145C199360897 @default.
• W2969031145 hasConceptScore W2969031145C205649164 @default.
• W2969031145 hasConceptScore W2969031145C2777851325 @default.
• W2969031145 hasConceptScore W2969031145C33923547 @default.
• W2969031145 hasConceptScore W2969031145C41008148 @default.
• W2969031145 hasConceptScore W2969031145C50644808 @default.
• W2969031145 hasConceptScore W2969031145C58489278 @default.
• W2969031145 hasConceptScore W2969031145C83546350 @default.
• W2969031145 hasFunder F4320325210 @default.
• W2969031145 hasFunder F4320325434 @default.
• W2969031145 hasLocation W29690311451 @default.

• next