Machine Learning and Knowledge Discovery in Databases Papeback -
by CALDERS, T. ET.AL
- New
A$154.51
A$5.85
Delivery within USA
Standard delivery: 9 to 14 days
More delivery options
Standard delivery: 9 to 14 days
Ships from Cold Books (New York, United States)
Details
- Title Machine Learning and Knowledge Discovery in Databases
- Author CALDERS, T. ET.AL
- Binding Papeback
- Condition New
- Publisher Springer
- Publication date pp. 756
- Features Illustrated
- Bookseller's Inventory # 6134180746
- ISBN 9783662448472
- Quantity available 4
About Cold Books New York, United States
Reader reviews for Machine Learning and Knowledge Discovery in Databases
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
From the rear cover
This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.