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1,114 datasets for “learning models”

  1. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 12 Feb 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  2. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 5 Mar 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  3. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 16 Mar 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  4. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 29 Mar 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  5. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 4 Apr 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  6. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 10 Apr 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  7. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 16 Apr 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

    • PDF
    • HTML
  8. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 28 Apr 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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    • HTML
  9. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 5 May 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

    • PDF
    • HTML
  10. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 4 Jun 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

    • PDF
    • HTML
  11. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 17 Jun 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

    • PDF
    • HTML
  12. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 23 Jun 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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    • HTML
  13. data.gov.au

    Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

    Australian Ocean Data NetworkUpdated 31 Jul 2026

    … The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region …

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  14. GovData Germany

    THE Benchmark: Transferable Representation Learning for Monocular Height Estimation

    open.bydataUpdated 13 Nov 2026

    Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most …

  15. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 17 Oct 2025

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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  16. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 20 Jan 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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    • HTML
  17. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 2 Mar 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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    • HTML
  18. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 7 Mar 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  19. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 25 Mar 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  20. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 20 Apr 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
“learning models” datasets · Open Data Finder