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EB

EB

ONLINE

First edition

Boca Raton, FL : CRC Press, 2016

1 online zdroj

ISBN 9781315371740 (e-kniha ; PDF)

ISBN 9781498703888

ISBN 9781315335407

ISBN 9781498703871

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Chapter 1 Network Science Perspectives on Engineering Adaptation to Climate Change and Weather Extremes / Udit Bhatia Auroop R. Ganguly -- chapter 2 Structured Estimation in High Dimensions -- Applications in Climate / Andre R Goncalves Arindam Banerjee Vidyashankar Sivakumar Soumyadeep Chatterjee -- chapter 3 Spatiotemporal Global Climate Model Tracking / Scott McQuade Claire Monteleoni -- chapter 4 Statistical Downscaling in Climate with State-of-the-Art Scalable Machine Learning / Thomas Vandal Udit Bhatia Auroop R. Ganguly -- chapter 5 Large-Scale Machine Learning for Species Distributions / Reid A. Johnson Jason D. K. Dzurisin Nitesh V. Chawla -- chapter 6 Using Large-Scale Machine Learning to Improve Our Understanding of the Formation of Tornadoes / Amy McGovern Corey Potvin Rodger A. Brown -- chapter 7 Deep Learning for Very High-Resolution Imagery Classification / Sangram Ganguly Saikat Basu Ramakrishna Nemani Supratik Mukhopadhyay Andrew Michaelis Petr Votava Cristina Milesi Uttam Kumar -- chapter 8 Unmixing Algorithms -- A Review of Techniques for Spectral Detection and Classification of Land Cover from Mixed Pixels on NASA Earth Exchange / Uttam Kumar Cristina Milesi S. Kumar Raja Ramakrishna Nemani Sangram Ganguly Weile Wang Petr Votava Andrew Michaelis Saikat Basu -- chapter 9 Semantic Interoperability of Long-Tail Geoscience Resources over the Web / Mostafa M. Elag Praveen Kumar Luigi Marini Scott D. Peckham Rui Liu.

"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 ...-.

Popsáo podle dat dodaných poskytovatelem

001478461

(OCoLC)993949309

(OCoLC-P)993949309

(TAFR)9781315371740