000			04658cam a2200493 i 4500
001			on1305912625
003			OCoLC
005			20250612155524.0
006			m o d
007			cr |n|||||||||
008			220326t20222022ne a ob 001 0 eng d
040			_aYDX _beng _erda _cYDX _dOPELS _dOCLCO _dEBLCP _dAFU _dN$T _dOCLCF _dSFB _dOSU _dOCLCQ _dOCLCO _dOCLCQ _dUKKRT _dOCLCQ
019			_a1305438600 _a1306021844
020			_a9780128187227 _q(electronic bk.)
020			_a0128187220 _q(electronic bk.)
020			_z9780128187210 _q(print)
020			_z0128187212 _q(print)
035			_a(OCoLC)1305912625 _z(OCoLC)1305438600 _z(OCoLC)1306021844
050		4	_aQB602.95 _b.M33 2022
082	0	4	_a523.20285631 _223
245	0	0	_aMachine learning for planetary science / _cedited by Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner.
264		1	_aAmsterdam : _bElsevier, _c2022.
264		4	_c�2022
300			_a1 online resource (xvi, 216 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
588	0		_aPrint version record.
505	0		_aFront 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
505	8		_a1.6 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
505	8		_a3.1.1 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
505	8		_a4.3.1 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
505	8		_a5.3 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
650		0	_aPlanetary science _xData processing. _934488
650		0	_aMachine learning.
700	1		_aHelbert, Joern, _eeditor. _934489
700	1		_aD'Amore, Mario, _eeditor. _934490
700	1		_aAye, Michael, _eeditor. _934491
700	1		_aKerner, Hannah, _eeditor. _934492
776	0	8	_iPrint version: _z0128187212 _z9780128187210 _w(OCoLC)1144876456
776	0	8	_iPrint version: _tMachine learning for planetary science _z9780128187210 _w(OCoLC)1285702618
856	4	0	_3ScienceDirect _uhttps://www.sciencedirect.com/science/book/9780128187210
999			_c216576 _d216576