000			05946cam a2200541Mi 4500
001			9781498703888
003			FlBoTFG
005			20220711212841.0
006			m o d
007			cr cn|||||||||
008			170717s2016 flua o 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781315371740 _q(e-book ; _qPDF)
020			_a131537174X
020			_a9781498703888
020			_a1498703887
020			_a9781315335407
020			_a1315335409
020			_a9781498703871
020			_a1498703879
035			_a(OCoLC)993949309 _z(OCoLC)1003989714 _z(OCoLC)1010963592 _z(OCoLC)1015206554 _z(OCoLC)1031042959 _z(OCoLC)1036280390
035			_a(OCoLC-P)993949309
050		4	_aQE48.87 _bS658 2016
082	0	4	_a550.2856312
100	1		_aSrivastava, Ashok N., _eauthor. _920183
245	1	0	_aLarge-Scale Machine Learning in the Earth Sciences / _cAshok N. Srivastava.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2016.
300			_a1 online resource : _btext file, PDF.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aChapman & Hall/CRC Data Mining and Knowledge Discovery Series
520	2		_a"From the Foreword:"While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by AshokSrivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest ... I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences."--Vipin Kumar, University of MinnesotaLarge-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book."--Provided by publisher.
505	0	0	_tChapter 1 Network Science Perspectives on Engineering Adaptation to Climate Change and Weather Extremes / _rUdit Bhatia Auroop R. Ganguly -- _tchapter 2 Structured Estimation in High Dimensions -- _tApplications in Climate / _rAndré R Goncalves Arindam Banerjee Vidyashankar Sivakumar Soumyadeep Chatterjee -- _tchapter 3 Spatiotemporal Global Climate Model Tracking / _rScott McQuade Claire Monteleoni -- _tchapter 4 Statistical Downscaling in Climate with State-of-the-Art Scalable Machine Learning / _rThomas Vandal Udit Bhatia Auroop R. Ganguly -- _tchapter 5 Large-Scale Machine Learning for Species Distributions / _rReid A. Johnson Jason D. K. Dzurisin Nitesh V. Chawla -- _tchapter 6 Using Large-Scale Machine Learning to Improve Our Understanding of the Formation of Tornadoes / _rAmy McGovern Corey Potvin Rodger A. Brown -- _tchapter 7 Deep Learning for Very High-Resolution Imagery Classification / _rSangram Ganguly Saikat Basu Ramakrishna Nemani Supratik Mukhopadhyay Andrew Michaelis Petr Votava Cristina Milesi Uttam Kumar -- _tchapter 8 Unmixing Algorithms -- _tA Review of Techniques for Spectral Detection and Classification of Land Cover from Mixed Pixels on NASA Earth Exchange / _rUttam Kumar Cristina Milesi S. Kumar Raja Ramakrishna Nemani Sangram Ganguly Weile Wang Petr Votava Andrew Michaelis Saikat Basu -- _tchapter 9 Semantic Interoperability of Long-Tail Geoscience Resources over the Web / _rMostafa M. Elag Praveen Kumar Luigi Marini Scott D. Peckham Rui Liu.
588			_aOCLC-licensed vendor bibliographic record.
650	0	7	_aCOMPUTERS _xMachine Theory. _2bisacsh _920184
650	0	7	_aSCIENCE _xEarth Sciences _xGeneral. _2bisacsh _920185
650		0	_aEarth sciences _xComputer network resources. _920186
650		0	_aEarth sciences _xData processing. _920187
700	1		_aNemani, Ramakrishna. _920188
700	1		_aSteinhaeuser, Karsten. _920189
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781498703888 _qapplication/PDF _zDistributed by publisher. Purchase or institutional license may be required for access.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781315371740 _zClick here to view.
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
938			_aTaylor & Francis _bTAFR _n9781315371740
942			_cEBK
999			_c72315 _d72315