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Advances in Machine Learning and Data Mining for Astronomy - Chapman & Hall / CRC Data Mining and Knowledge Discovery Series 1st edition
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Advances in Machine Learning and Data Mining for Astronomy - Chapman & Hall / CRC Data Mining and Knowledge Discovery Series
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science.
The book?s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.
With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
744 pages, 177 black & white illustrations, 33 colour illustrations, 15 black & white tables
| Media | Books Paperback Book (Book with soft cover and glued back) |
| Released | November 16, 2016 |
| ISBN13 | 9781138199309 |
| Publishers | Taylor & Francis Ltd |
| Pages | 744 |
| Dimensions | 150 × 220 × 10 mm · 1.38 kg |
| Language | English |
| Editor | Ali, Kamal M. (Metric Avenue, San Francisco, California, USA) |
| Editor | Scargle, Jeffrey D. (NASA Ames Research Center, Moffett Field, California, USA) |
| Editor | Srivastava, Ashok N. (Verizon, California, USA) |
| Editor | Way, Michael J. (NASA Goddard Institute for Space Studies, New York, New York, USA) |
