Entropy in Machine Learning Applications
The aim of this reprint is to inform readers of the latest developments in methods and applications of machine learning and deep learning in certain fields, including the following: a semantically enhanced social network user alignment algorithm to perform user alignment; a congestion control mechan...
Đã lưu trong:
| Định dạng: | Online |
|---|---|
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Những chủ đề: | |
| Truy cập trực tuyến: | ONIX_20250812T095121_9783725830657_33 |
| Các nhãn: |
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| _version_ | 1869530338526494720 |
|---|---|
| collection | Directory of Open Access Books |
| description | The aim of this reprint is to inform readers of the latest developments in methods and applications of machine learning and deep learning in certain fields, including the following: a semantically enhanced social network user alignment algorithm to perform user alignment; a congestion control mechanism based on deep reinforcement learning; problem solving involving low-accuracy, large-entropy perturbation; information loss in the calculation process of fault feature parameters of rolling bearing; a hybrid recommendation model combining autoencoder and latent feature analysis techniques; extracting knowledge from published papers and reports on drilling to guide the control of wells; an improved binary golden jackal optimization algorithm; water quality prediction based on machine learning and comprehensive weighting methods; redundancy of crossentropy calculation in deep learning of classifiers; automatic vertebral rotation angle measurement of vertebrae using an improved transformer network; defining suitable graph contrastive learning through applications of graph information bottlenecks and structural entropy theories; and a comprehensive examination of the latest advancements in deep learning methodologies. We hope that the papers in this Special Issue can contribute to promoting and facilitating the further research and application of machine learning methods. |
| format | Online |
| id | doab-20.500.12854ir-165084 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1650842025-08-12T08:00:10Z Entropy in Machine Learning Applications Liang, Yanchun knowledge graph data correlation differential expression long–short-term memory semantic disambiguation advertising vocabulary entity relationship extraction semi-supervised learning cross-entropy loss semantic entropy thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology The aim of this reprint is to inform readers of the latest developments in methods and applications of machine learning and deep learning in certain fields, including the following: a semantically enhanced social network user alignment algorithm to perform user alignment; a congestion control mechanism based on deep reinforcement learning; problem solving involving low-accuracy, large-entropy perturbation; information loss in the calculation process of fault feature parameters of rolling bearing; a hybrid recommendation model combining autoencoder and latent feature analysis techniques; extracting knowledge from published papers and reports on drilling to guide the control of wells; an improved binary golden jackal optimization algorithm; water quality prediction based on machine learning and comprehensive weighting methods; redundancy of crossentropy calculation in deep learning of classifiers; automatic vertebral rotation angle measurement of vertebrae using an improved transformer network; defining suitable graph contrastive learning through applications of graph information bottlenecks and structural entropy theories; and a comprehensive examination of the latest advancements in deep learning methodologies. We hope that the papers in this Special Issue can contribute to promoting and facilitating the further research and application of machine learning methods. 2025-08-12T08:00:08Z 2025-08-12T08:00:08Z 2025 book ONIX_20250812T095121_9783725830657_33 9783725830657 9783725830664 https://directory.doabooks.org/handle/20.500.12854/165084 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10542 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3066-4 10.3390/books978-3-7258-3066-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725830657 9783725830664 244 open access |
| spellingShingle | knowledge graph data correlation differential expression long–short-term memory semantic disambiguation advertising vocabulary entity relationship extraction semi-supervised learning cross-entropy loss semantic entropy thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Entropy in Machine Learning Applications |
| title | Entropy in Machine Learning Applications |
| title_full | Entropy in Machine Learning Applications |
| title_fullStr | Entropy in Machine Learning Applications |
| title_full_unstemmed | Entropy in Machine Learning Applications |
| title_short | Entropy in Machine Learning Applications |
| title_sort | entropy in machine learning applications |
| topic | knowledge graph data correlation differential expression long–short-term memory semantic disambiguation advertising vocabulary entity relationship extraction semi-supervised learning cross-entropy loss semantic entropy thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | knowledge graph data correlation differential expression long–short-term memory semantic disambiguation advertising vocabulary entity relationship extraction semi-supervised learning cross-entropy loss semantic entropy thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20250812T095121_9783725830657_33 |