Difference between revisions of "ECML PKDD 2009"
Jump to navigation
Jump to search
(modified through wikirestore by orapi) |
(modified through wikirestore by orapi) |
||
(2 intermediate revisions by the same user not shown) | |||
Line 1: | Line 1: | ||
{{Event | {{Event | ||
− | |||
− | |||
− | |||
− | |||
| Field = Machine learning | | Field = Machine learning | ||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
| Abstract deadline = | | Abstract deadline = | ||
| Submission deadline = Apr 20, 2009 | | Submission deadline = Apr 20, 2009 | ||
| Notification = Jun 10, 2009 | | Notification = Jun 10, 2009 | ||
| Camera ready = Jun 20, 2009 | | Camera ready = Jun 20, 2009 | ||
+ | | Acronym= ECML PKDD 2009 | ||
+ | | End date= 2009-09-11 | ||
+ | | Series= ECML PKDD | ||
+ | | Type = Conference | ||
+ | | Country= SI | ||
+ | | State = SI/003 | ||
+ | | City = SI/003/Bled | ||
+ | | Year = 2009 | ||
+ | | Homepage= www.ecmlpkdd2009.net/ | ||
+ | | Start date= 2009-09-07 | ||
+ | | Title = The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | ||
}} | }} | ||
Latest revision as of 04:05, 6 December 2021
Event Rating
median | worst |
---|---|
![]() |
![]() |
List of all ratings can be found at ECML PKDD 2009/rating
ECML PKDD 2009 | |
---|---|
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
| |
Event in series | ECML PKDD |
Dates | 2009-09-07 (iCal) - 2009-09-11 |
Homepage: | www.ecmlpkdd2009.net/ |
Location | |
Location: | SI/003/Bled, SI/003, SI |
Important dates | |
Submissions: | Apr 20, 2009 |
Notification: | Jun 10, 2009 |
Camera ready due: | Jun 20, 2009 |
Table of Contents | |
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases provides an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. Encouraged are submissions of papers that describe the application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques. Submissions that demonstrate both theoretical and empirical rigor are especially encouraged.