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SCIDIR_on1305912625 |
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OCoLC |
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20231120010638.0 |
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m o d |
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220326t20222022ne a ob 001 0 eng d |
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|a YDX
|b eng
|e rda
|c YDX
|d OPELS
|d OCLCO
|d EBLCP
|d AFU
|d N$T
|d OCLCF
|d SFB
|d OSU
|d OCLCQ
|d OCLCO
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| 019 |
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|a 1305438600
|a 1306021844
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| 020 |
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|a 9780128187227
|q (electronic bk.)
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| 020 |
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|a 0128187220
|q (electronic bk.)
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| 020 |
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|z 9780128187210
|q (print)
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| 020 |
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|z 0128187212
|q (print)
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| 035 |
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|a (OCoLC)1305912625
|z (OCoLC)1305438600
|z (OCoLC)1306021844
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| 050 |
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4 |
|a QB602.95
|b .M33 2022
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| 082 |
0 |
4 |
|a 523.20285631
|2 23
|
| 245 |
0 |
0 |
|a Machine learning for planetary science /
|c edited by Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner.
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| 264 |
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1 |
|a Amsterdam :
|b Elsevier,
|c 2022.
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| 264 |
|
4 |
|c �2022
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| 300 |
|
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|a 1 online resource (xvi, 216 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
|
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|a Includes bibliographical references and index.
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| 588 |
0 |
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|a Print version record.
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| 505 |
0 |
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|a 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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| 505 |
8 |
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|a 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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| 505 |
8 |
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|a 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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| 505 |
8 |
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|a 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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| 505 |
8 |
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|a 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
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| 650 |
|
0 |
|a Planetary science
|x Data processing.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
6 |
|a Plan�etologie
|0 (CaQQLa)201-0138968
|x Informatique.
|0 (CaQQLa)201-0380011
|
| 650 |
|
6 |
|a Apprentissage automatique.
|0 (CaQQLa)201-0131435
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|0 (OCoLC)fst01004795
|
| 700 |
1 |
|
|a Helbert, Joern,
|e editor.
|
| 700 |
1 |
|
|a D'Amore, Mario,
|e editor.
|
| 700 |
1 |
|
|a Aye, Michael,
|e editor.
|
| 700 |
1 |
|
|a Kerner, Hannah,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|z 0128187212
|z 9780128187210
|w (OCoLC)1144876456
|
| 776 |
0 |
8 |
|i Print version:
|t Machine learning for planetary science
|z 9780128187210
|w (OCoLC)1285702618
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128187210
|z Texto completo
|
