Informed Machine Learning
This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combina...
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| Μορφή: | Online |
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| Γλώσσα: | Αγγλικά |
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Springer Nature
2025
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20250414_9783031830976_16 |
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| _version_ | 1869527303005929472 |
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| collection | Directory of Open Access Books |
| description | This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applications where the aspect of small data sets is a challenge. Machine Learning with small amounts of data? After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of “Informed Machine Learning” comes into play. Informed Machine Learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable. |
| format | Online |
| id | doab-20.500.12854ir-158451 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1584512025-07-30T09:00:15Z Informed Machine Learning Schulz, Daniel Bauckhage, Christian Informed Machine Learning Anomaly Detection Interpretable Model Deep Learning Knowledge Graphs Graph Neural Networks AITwin Bayesian Inference Multi-Agent Neural Rewriter Support Vector Machines Multivariate Time Series Differential Equations thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applications where the aspect of small data sets is a challenge. Machine Learning with small amounts of data? After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of “Informed Machine Learning” comes into play. Informed Machine Learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable. 2025-04-15T04:22:33Z 2025-04-15T04:22:33Z 2025-04-14T12:56:48Z 2025 book ONIX_20250414_9783031830976_16 https://library.oapen.org/handle/20.500.12657/100755 9783031830969 https://directory.doabooks.org/handle/20.500.12854/158451 eng Cognitive Technologies open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/100755/1/9783031830976.pdf Springer Nature Springer Nature Switzerland 10.1007/978-3-031-83097-6 10.1007/978-3-031-83097-6 9fa3421d-f917-4153-b9ab-fc337c396b5a 15487bd0-7fc0-4a38-ba36-e15c641fcf45 9783031830969 Springer Nature Switzerland 339 Cham [...] open access |
| spellingShingle | Informed Machine Learning Anomaly Detection Interpretable Model Deep Learning Knowledge Graphs Graph Neural Networks AITwin Bayesian Inference Multi-Agent Neural Rewriter Support Vector Machines Multivariate Time Series Differential Equations thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems Informed Machine Learning |
| title | Informed Machine Learning |
| title_full | Informed Machine Learning |
| title_fullStr | Informed Machine Learning |
| title_full_unstemmed | Informed Machine Learning |
| title_short | Informed Machine Learning |
| title_sort | informed machine learning |
| topic | Informed Machine Learning Anomaly Detection Interpretable Model Deep Learning Knowledge Graphs Graph Neural Networks AITwin Bayesian Inference Multi-Agent Neural Rewriter Support Vector Machines Multivariate Time Series Differential Equations thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems |
| topic_facet | Informed Machine Learning Anomaly Detection Interpretable Model Deep Learning Knowledge Graphs Graph Neural Networks AITwin Bayesian Inference Multi-Agent Neural Rewriter Support Vector Machines Multivariate Time Series Differential Equations thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems |
| url | ONIX_20250414_9783031830976_16 |