Advances in machine learning and data mining for astronomy
"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…
Read More ↓Title Statements
- Full title :Advances in machine learning and data mining for astronomy / editors, Michael J. Way [and others].
Summary
"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"--
Read More ↓Edition Statements
- Publication Date:©2012.
Publication Statements
- Publisher(s):Chapman and Hall/CRC
- Place of Publication:Boca Raton
- Language:eng
- Publication Date:©2012.
The Physical Object
- Description:1 online resource : : color illustrations
Edition Identifiers
- ISBN:9781439841747, 1439841748
- oclc:ocn785415701
- LC Classification:QB51.3.E43
- Dewey Decimal:522/.85631
Table of Contents
Front Cover; Contents; Foreword; Editors; Perspective; Contributors; PART I: Foundational Issues; CHAPTER 1: Classification in Astronomy: Past and Present; CHAPTER 2: Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy; CHAPTER 3: Probability and Statistics in Astronomical Machine Learning and Data Mining; PART II: Astronomical Applications; SECTION 1: Source Identification; CHAPTER 4: Automated Science Processing for the Fermi Large Area Telescope; CHAPTER 5: Cosmic Microwave Background Data Analysis.
Read More ↓Subjects
Writers
More from this Series
No reviews yet. Be the first to rate this work.
Related Works
more →
Neural networks with R: smart models using CNN, RNN, deep learn…
Ciabuschi, Francesco
Learning decision sequences for repetitive processes-selected algorithms
Rafajłowicz, Wojciech
Document processing using machine learning
Machine learning pocket reference: working with structured data in Python
Harrison, Matt
Machine learning pocket reference: working with structured data in Python
Harrison, Matt
Practical time series analysis: master time series data processing, vis…
Pal, Avishek
Visual attributes
Files & Downloads
-
Advances in machine learning and data mining for …
PDF · 14.0 MB · updated -
Advances in Machine Learning and Data Mining for …
PDF · 18.0 MB · updated
Rate & Review