Advances in Machine Learning and Data Mining for Astronomy

Name: Advances in Machine Learning and Data Mining for Astronomy
Brand: Taylor & Francis Inc
Price: 280.99 AUD
Availability: BackOrder

By: Ashok N. Srivastava (Editor), Michael J. Way (Editor), Kamal M. Ali (Editor), Jeffrey D. Scargle (Editor)

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Hardcover | 29 March 2012 | Edition Number 1

At a Glance

Hardcover
744 Pages

Dimensions(cm)
25.9 x 18.0 x 4.8

Hardcover

RRP $326.00

$280.99

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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.

Industry Reviews

"The volume is a well-organised collection of articles presenting the importance of modern data mining and machine learning techniques in application to analysis of astronomical data. ... A major strength of the volume is its very impressive collection of real examples that can be both inspirational and educational. ... The book is particularly successful in showing how collaboration between computer scientists and statisticians on one side and astronomers on the other is needed to search for a scientific discovery in the abundance of data generated by instrumentation and simulations."
-Krzysztof Podgorski, International Statistical Review, 2014

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