Advances in Machine Learning and Data Mining for Astronomy
List Price:
$72.99
- Availability: Confirm prior to ordering
- Branding: minimum 50 pieces (add’l costs below)
- Check Freight Rates (branded products only)
Branding Options (v), Availability & Lead Times
- 1-Color Imprint: $2.00 ea.
- Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
- Belly-Band Wrap: $2.50 ea. (full-color printed)
- Set-Up Charge: $45 per decoration
- Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
- Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
- Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
- RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
Product Details
Author:
Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava
Format:
Paperback
Pages:
744
Publisher:
CRC Press (November 16, 2016)
Language:
English
ISBN-13:
9781138199309
Weight:
48.75oz
Dimensions:
7" x 10"
File:
TAYLORFRANCIS-TayFran_260825150022844-20260825.xml
Folder:
TAYLORFRANCIS
List Price:
$72.99
Country of Origin:
United States
Series:
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Case Pack:
10
As low as:
$69.34
Publisher Identifier:
P-CRC
Discount Code:
H
Pub Discount:
30
Imprint:
Chapman and Hall/CRC
Overview
This practical guide illustrates the use of state-of-the-art machine learning and data mining techniques in astronomy. The book presents issues in the astronomical sciences that are also important to health, social, and physical sciences. It describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In addition, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.
