Identification of Relevant Relationships in Data Using Machine Learning (English)
- New search for: Hammesfahr, Joshua
- New search for: Spott, Martin
- New search for: Barton, Thomas
- New search for: Müller, Christian
- New search for: Hammesfahr, Joshua
- New search for: Spott, Martin
In:
Apply Data Science
: Introduction, Applications and Projects
;
Chapter: 12
;
189-205
;
2023
- Article/Chapter (Book) / Electronic Resource
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Title:Identification of Relevant Relationships in Data Using Machine Learning
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Contributors:Barton, Thomas ( editor ) / Müller, Christian ( editor ) / Hammesfahr, Joshua ( author ) / Spott, Martin ( author )
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Published in:Apply Data Science : Introduction, Applications and Projects ; Chapter: 12 ; 189-205
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Publisher:
- New search for: Springer Fachmedien Wiesbaden
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Place of publication:Wiesbaden
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Publication date:2023-01-01
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Size:17 pages
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ISBN:
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DOI:
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Type of media:Article/Chapter (Book)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents eBook
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 1
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Data Science: From Concept to ApplicationBarton, Thomas / Müller, Christian et al. | 2023
- 2
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Visualization and Deep Learning in Data ScienceKaufmann, Jens / Retkowitz, Daniel et al. | 2023
- 3
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Digital Ethics in Data-Driven Organizations and AI Ethics as Application ExampleLemke, Claudia / Monett, Dagmar / Mikoleit, Manuel et al. | 2023
- 4
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Multiple Perspectives for the Implementation of Innovative Technological Solutions in the Context of Data-Driven Decision-MakingNitsche, Anna-Maria / Schumann, Christian-Andreas / Laroque, Christoph / Matthias, Olga et al. | 2023
- 5
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Don’t Be Afraid of Failure—Insights from a Survey on the Failure of Data Science ProjectsAßmann, Jule / Sauer, Joachim / Schulz, Michael et al. | 2023
- 6
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Recommendation Systems and the Use of Machine Learning MethodsPeuker, Andreas / Barton, Thomas et al. | 2023
- 7
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Comparison of Machine Learning Functionalities of Business Intelligence and Analytics ToolsRoth-Dietrich, Gabriele / Gröschel, Michael / Reiner, Benedikt et al. | 2023
- 8
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Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and RolesKuehnel, Stephan / Neuhaus, Uwe / Kaufmann, Jens / Schulz, Michael / Alekozai, Emal M. et al. | 2023
- 9
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Integration of Renewable Energies—AI-Based Prediction Methods for Electricity Generation from Photovoltaic SystemsBrandherm, Boris / Deru, Matthieu / Ndiaye, Alassane / Kiefer, Gian-Luca / Baus, Jörg / Gampfer, Ralf et al. | 2023
- 10
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Machine Learning for Energy Management OptimizationRoth-Dietrich, Gabriele / Gerten, Rainer et al. | 2023
- 11
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Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing ContentBarton, Thomas / Kokoev, Arthur et al. | 2023
- 12
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Identification of Relevant Relationships in Data Using Machine LearningHammesfahr, Joshua / Spott, Martin et al. | 2023
- 13
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Framework for the Management and Analysis of Vehicle Data for Model-Based Driver Assistance System Development in Teaching and ResearchPeuschke-Bischof, Tobias / Kubica, Stefan et al. | 2023
- 14
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Correction to: Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and RolesKuehnel, Stephan / Neuhaus, Uwe / Kaufmann, Jens / Schulz, Michael / Alekozai, Emal M. et al. | 2023