Machine Learning: ECML 2006 - 2006

Get your print report emailed

Enter your email and we can send you a printable report on this item. ( View sample )

Print report emailed

We'll send you a print report shortly at Unknown

Receive our monthly book update for this book

We will send you an email each month containing links to all the book updates you subscribe to. This will keep informed of all the activity around your favourite books.


You will now receive reports at unknown email address

Share this page with others

You can share this item across the most popular social networks.

Click on the button below to purchase Machine Learning: ECML 2006

Buy book

When you click on the button above, you will be redirected to where you can finish shopping.

Machine Learning: ECML 2006

Crossref Citations Summary

Crossref Citations by year

Crossref Citations by Chapter

No online mentions found

Mendeley readership by country

This book has 1002 Mendeley readers (combined total for all chapters).
Click here to see more details on the Mendeley website.

Country %
United States 46 5%
France 34 3%
United Kingdom 17 2%
Germany 12 1%
China 10 1%
Spain 10 1%
Canada 6 1%
Japan 6 1%
Brazil 5 0%
Other 61 6%
Unknown 795 79%

Mendeley readership by discipline

Discipline %
Computer Science 689 69%
Engineering 108 11%
Agricultural and Biological Sciences 42 4%
Mathematics 31 3%
Computer and Information Science 24 2%
Other 104 10%
Unknown 4 0%

Mendeley readership by professional status

Professional status %
Student > Ph. D. Student 342 34%
Researcher 190 19%
Student > Master 170 17%
Student > Bachelor 59 6%
Professor > Associate Professor 45 4%
Other 192 19%
Unknown 4 0%

SpringerLink Download summary

The combined chapter downloads for this book are 114,001.


  • "In this book, we find many ways of representing machine learning from different fields, including active learning, algorithmic learning, case-based learning, classifier systems, clustering algorithms, decision-tree learning, inductive inference, kernel methods, knowledge discovery, multiple-instance learning, reinforcement learning, statistical learning, and support vector machines. Most of the current issues in machine learning research are discussed. … I strongly recommend this book for all researchers interested in the very best of machine learning studies."

    Agliberto Cierco, ACM Computing Reviews, Vol. 49 (5), 2008