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Advances in Machine Learning and Data Mining for Astronomy
— —
Michael J. Way (NASA Goddard Institute for Space Studies, New York, New York, USA) Jeffrey D. Scargle (NASA Ames Research Center, Moffett Field, California, USA)
Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way (NASA Goddard Institute for Space Studies, New York, New York, USA) at Abbey's Bookshop,

Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way (NASA Goddard Institute for Space Studies, New York, New York, USA) Jeffrey D. Scargle (NASA Ames Research Center, Moffett Field, California, USA) Kamal M. Ali (Metric Avenue, San Francisco, California, USA) Ashok N. Srivastava (Verizon, California, USA)


9781439841730

Whittles Publishing


Astronomy, space & time;
Data mining;
Machine learning


Hardback

744 pages

$242.00
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This book provides a comprehensive overview of various data mining tools and techniques that are increasingly being used by researchers in the international astronomy community. It explores this new problem domain, discussing how it could lead to the development of entirely new algorithms. Leading contributors introduce data mining methods and then describe how the methods can be implemented into astronomy applications. The last section of the book discusses the Redshift Prediction Competition, which is an astronomy competition in the style of the Netflix Prize.

Edited by:   Michael J. Way (NASA Goddard Institute for Space Studies New York New York USA), Jeffrey D. Scargle (NASA Ames Research Center, Moffett Field, California, USA), Kamal M. Ali (Metric Avenue, San Francisco, California, USA), Ashok N. Srivastava (Verizon, California, USA)
Imprint:   Whittles Publishing
Country of Publication:   United Kingdom
Volume:   No. 24
Dimensions:   Height: 254mm,  Width: 178mm,  Spine: 48mm
Weight:   1.565kg
ISBN:   9781439841730
ISBN 10:   143984173X
Series:   Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Pages:   744
Publication Date:   March 2012
Audience:   College/higher education ,  Primary
Format:   Hardback
Publisher's Status:   Active

Michael J. Way, PhD, is a research scientist at the NASA Goddard Institute for Space Studies in New York and the NASA Ames Research Center in California. He is also an adjunct professor in the Department of Physics and Astronomy at Hunter College. His research focuses on understanding the multiscale structure of our universe, modeling the atmospheres of exoplanets, and applying kernel methods to new areas in astronomy. Jeffrey D. Scargle, PhD, is an astrophysicist in the Space Science and Astrobiology Division of the NASA Ames Research Center. His main interests encompass the variability of astronomical objects, including the Sun, sources in the Galaxy, and active galactic nuclei; cosmology; plasma astrophysics; planetary detection; and data analysis and statistical methods. Kamal M. Ali, PhD, is a research scientist in machine learning and data mining. He has a consulting practice and is cofounder of the start-up Metric Avenue. He has carried out research at IBM Almaden, Stanford University, Vividence, Yahoo, and TiVo, where he worked on the Tivo Collaborative Filtering Engine. His current research focuses on combining machine learning in conditional random fields with linguistically rich features to make machines better at reading web pages. Ashok N. Srivastava, PhD, is the principal scientist for Data Mining and Systems Health Management and leader of the Intelligent Data Understanding group at NASA Ames Research Center. His research includes the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms.


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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