Improving relational aggregated search from big data sources using stacked autoencoders
Sign Up to like & getrecommendations! Published in 2018 at "Cognitive Systems Research"
DOI: 10.1016/j.cogsys.2018.05.002
Abstract: Abstract Relational aggregated search (RAS) is defined as a complementary set of approaches in which the relations between information nuggets are taken into account. From this viewpoint, the relational aggregated search should retrieve information nuggets… read more here.
Keywords: stacked autoencoders; big data; relational aggregated; search ... See more keywords
Data-Enhanced Stacked Autoencoders for Insufficient Fault Classification of Machinery and its Understanding via Visualization
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2985769
Abstract: As a practical tool for big data processing, deep learning not only has drawn extensive attentions in the inherent law and representation level of sample data, but also has been widely concerned in the field… read more here.
Keywords: data enhanced; fault classification; enhanced stacked; stacked autoencoders ... See more keywords
Improving stacked-autoencoders with 1D convolutional-nets for hyperspectral image land-cover classification
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Applied Remote Sensing"
DOI: 10.1117/1.jrs.15.026506
Abstract: Abstract. Deep learning opened new possibilities for remote sensing image analysis using multiple neural nets layers. We introduce a hybrid pixel-based model that allows improving the unsupervised training with stacked autoencoders (SAE) by inserting convolutional… read more here.
Keywords: autoencoders convolutional; improving stacked; stacked autoencoders; image ... See more keywords