| Description |
1 online resource (xvi, 216 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Print version record. |
| Contents |
Front Cover -- Machine Learning for Planetary Science -- Copyright -- Contents -- Contributors -- Foreword -- References -- 1 Introduction to machine learning -- 1.1 Overview of machine learning methods -- 1.2 Supervised learning -- 1.2.1 Classification -- 1.2.2 Regression -- 1.3 Unsupervised learning -- 1.3.1 Clustering -- 1.3.2 Dimensionality reduction -- 1.4 Semisupervised learning -- 1.4.1 Self-training -- 1.4.2 Self-training with Expectation Maximization -- 1.4.3 Cotraining -- 1.5 Active learning -- 1.5.1 Uncertainty sampling -- 1.5.2 Query-by-committee |
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1.6 Popular machine learning methods -- 1.6.1 Principal component analysis -- 1.6.2 K-means clustering -- 1.6.3 Support vector machines -- 1.6.4 Decision trees and random forests -- 1.6.5 Neural networks -- 1.7 Data set preparation -- References -- 2 The new and unique challenges of planetary missions -- 2.1 Introduction -- 2.1.1 50 years of Mercury exploration -- 2.1.2 Challenges of large and complex data return -- 2.1.3 Facing the unknown -- 2.1.4 Machine learning for planetary science -- References -- 3 Finding and reading planetary data -- 3.1 Data acquisition in planetary science |
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3.1.1 Introduction -- 3.1.2 Data processing levels -- 3.1.3 PDS -- 3.1.3.1 Organizational structure within a node -- Releases and volumes -- EDR and RDR -- PDS4 collections and bundles -- 3.1.4 ESA's Planetary Science Archive -- 3.1.5 Reading data with Python -- 3.1.5.1 Example reading of PDS3 data -- 3.1.5.2 Troubleshooting data reading -- 3.1.6 Spaces to watch -- 3.1.6.1 PDR -- 3.1.6.2 PlanetaryPy -- 3.1.6.3 OpenPlanetary -- 4 Introduction to the Python Hyperspectral Analysis Tool (PyHAT) -- 4.1 Introduction -- 4.2 PyHAT library architecture -- 4.3 PyHAT orbital |
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4.3.1 Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) -- 4.3.2 Moon Mineralogy Mapper (M3) -- 4.3.3 Kaguya Spectral Profiler -- 4.4 PyHAT in-situ -- 4.4.1 Baseline removal example -- 4.4.2 Regression analysis example -- 4.4.3 Data exploration example -- 4.4.4 Calibration transfer -- 4.5 Conclusion -- Acronyms -- Acknowledgments -- References -- 5 Tutorial: how to access, process, and label PDS image data for machine learning -- 5.1 Introduction -- 5.2 Access to PDS data products -- 5.2.1 PDS Image Atlas -- 5.2.2 PDS Imaging Node Data Portal |
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5.3 Preprocessing PDS data products into standard image formats -- 5.3.1 PDS image data products -- 5.3.2 PDS browse images -- 5.3.3 Converting PDS image data products -- 5.4 Labeling image data -- 5.4.1 Publicly available labeled image data sets -- 5.4.2 Tools for labeling image data -- 5.5 Example PDS image classifier results -- 5.5.1 Train, validation, and test sets -- 5.5.2 Model fine-tuning -- 5.5.3 Model calibration and performance -- 5.5.4 Access to HiRISENet classification results -- 5.6 Summary -- Acknowledgments -- References |
| Subject |
Planetary science -- Data processing.
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Machine learning.
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Planétologie -- Informatique.
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Apprentissage automatique.
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Machine learning. (OCoLC)fst01004795
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| Added Author |
Helbert, Joern, editor.
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D'Amore, Mario, editor.
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Aye, Michael, editor.
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Kerner, Hannah, editor.
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| Other Form: |
Print version: 0128187212 9780128187210 (OCoLC)1144876456 |
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Print version: Machine learning for planetary science 9780128187210 (OCoLC)1285702618 |
| ISBN |
9780128187227 (electronic bk.) |
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0128187220 (electronic bk.) |
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9780128187210 (print) |
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0128187212 (print) |
| Standard No. |
AU@ 000071513170 |
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AU@ 000071982080 |
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