FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220888961> ?p ?o ?g. }

• W4220888961 endingPage "22405" @default.
• W4220888961 startingPage "22379" @default.
• W4220888961 abstract "The concept of transfer learning has received a great deal of concern and interest throughout the last decade. Selecting an ideal representational framework for instances of various domains to minimize the divergence among source and target domains is a fundamental research challenge in representative transfer learning. The domain adaptation approach is designed to learn more robust or higher-level features, required in transfer learning. This paper presents a novel transfer learning framework that employs a marginal probability-based domain adaptation methodology followed by a deep autoencoder. The proposed frame adapts the source and target domain by plummeting distribution deviation between the features of both domains. Further, we adopt the deep neural network process to transfer learning and suggest a supervised learning algorithm based on encoding and decoding layer architecture. Moreover, we have proposed two different variants of the transfer learning techniques for classification, which are termed as (i) Domain adapted transfer learning with deep autoencoder-1 (D-TLDA-1) using the linear regression and (ii) Domain adapted transfer learning with deep autoencoder-2 (D-TLDA-2) using softmax regression. Simulations have been conducted with two popular real-world datasets: ImageNet datasets for image classification problem and 20_Newsgroups datasets for text classification problem. Experimental findings have established and the resulting improvements in accuracy measure of classification shows the supremacy of the proposed D-TLDA framework over prominent state-of-the-art machine learning and transfer learning approaches." @default.
• W4220888961 created "2022-04-03" @default.
• W4220888961 creator A5003623633 @default.
• W4220888961 creator A5068942151 @default.
• W4220888961 creator A5077233893 @default.
• W4220888961 creator A5081005098 @default.
• W4220888961 date "2022-03-16" @default.
• W4220888961 modified "2023-09-25" @default.
• W4220888961 title "Deep autoencoder based domain adaptation for transfer learning" @default.
• W4220888961 cites W1963579367 @default.
• W4220888961 cites W1986614398 @default.
• W4220888961 cites W1996303439 @default.
• W4220888961 cites W1998894210 @default.
• W4220888961 cites W2016202900 @default.
• W4220888961 cites W2016880913 @default.
• W4220888961 cites W2025198378 @default.
• W4220888961 cites W2037265949 @default.
• W4220888961 cites W2050752817 @default.
• W4220888961 cites W2062518264 @default.
• W4220888961 cites W2076167926 @default.
• W4220888961 cites W2097360283 @default.
• W4220888961 cites W2100664256 @default.
• W4220888961 cites W2115403315 @default.
• W4220888961 cites W2117130368 @default.
• W4220888961 cites W2119187866 @default.
• W4220888961 cites W2132339004 @default.
• W4220888961 cites W2146807976 @default.
• W4220888961 cites W2163345210 @default.
• W4220888961 cites W2165698076 @default.
• W4220888961 cites W2183585960 @default.
• W4220888961 cites W2226984459 @default.
• W4220888961 cites W2246782854 @default.
• W4220888961 cites W2336938381 @default.
• W4220888961 cites W2363300041 @default.
• W4220888961 cites W2395579298 @default.
• W4220888961 cites W2439924743 @default.
• W4220888961 cites W2478431555 @default.
• W4220888961 cites W2507341862 @default.
• W4220888961 cites W2520176975 @default.
• W4220888961 cites W2577325523 @default.
• W4220888961 cites W2758375579 @default.
• W4220888961 cites W2766328202 @default.
• W4220888961 cites W2786808285 @default.
• W4220888961 cites W2887280559 @default.
• W4220888961 cites W2917014164 @default.
• W4220888961 cites W2955109214 @default.
• W4220888961 cites W2963999631 @default.
• W4220888961 cites W2978165524 @default.
• W4220888961 cites W3041133507 @default.
• W4220888961 cites W3133605945 @default.
• W4220888961 cites W3135939397 @default.
• W4220888961 cites W3146610246 @default.
• W4220888961 cites W3157473606 @default.
• W4220888961 cites W3173469406 @default.
• W4220888961 cites W3196077619 @default.
• W4220888961 cites W3197613037 @default.
• W4220888961 cites W3198032304 @default.
• W4220888961 cites W4230674625 @default.
• W4220888961 cites W4231109964 @default.
• W4220888961 doi "https://doi.org/10.1007/s11042-022-12226-2" @default.
• W4220888961 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35310888" @default.
• W4220888961 hasPublicationYear "2022" @default.
• W4220888961 type Work @default.
• W4220888961 citedByCount "5" @default.
• W4220888961 countsByYear W42208889612023 @default.
• W4220888961 crossrefType "journal-article" @default.
• W4220888961 hasAuthorship W4220888961A5003623633 @default.
• W4220888961 hasAuthorship W4220888961A5068942151 @default.
• W4220888961 hasAuthorship W4220888961A5077233893 @default.
• W4220888961 hasAuthorship W4220888961A5081005098 @default.
• W4220888961 hasBestOaLocation W42208889611 @default.
• W4220888961 hasConcept C101738243 @default.
• W4220888961 hasConcept C108583219 @default.
• W4220888961 hasConcept C119857082 @default.
• W4220888961 hasConcept C138885662 @default.
• W4220888961 hasConcept C150899416 @default.
• W4220888961 hasConcept C153180895 @default.
• W4220888961 hasConcept C154945302 @default.
• W4220888961 hasConcept C171041071 @default.
• W4220888961 hasConcept C188441871 @default.
• W4220888961 hasConcept C2779178101 @default.
• W4220888961 hasConcept C41008148 @default.
• W4220888961 hasConcept C41895202 @default.
• W4220888961 hasConcept C59404180 @default.
• W4220888961 hasConceptScore W4220888961C101738243 @default.
• W4220888961 hasConceptScore W4220888961C108583219 @default.
• W4220888961 hasConceptScore W4220888961C119857082 @default.
• W4220888961 hasConceptScore W4220888961C138885662 @default.
• W4220888961 hasConceptScore W4220888961C150899416 @default.
• W4220888961 hasConceptScore W4220888961C153180895 @default.
• W4220888961 hasConceptScore W4220888961C154945302 @default.
• W4220888961 hasConceptScore W4220888961C171041071 @default.
• W4220888961 hasConceptScore W4220888961C188441871 @default.
• W4220888961 hasConceptScore W4220888961C2779178101 @default.
• W4220888961 hasConceptScore W4220888961C41008148 @default.
• W4220888961 hasConceptScore W4220888961C41895202 @default.
• W4220888961 hasConceptScore W4220888961C59404180 @default.
• W4220888961 hasIssue "16" @default.

• next