Difference between revisions of "ECML PKDD 2009"

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{{Event
 
{{Event
| Acronym = ECML PKDD 2009
 
| Title = The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
 
| Type = Conference
 
| Series = ECML PKDD
 
 
  | Field = Machine learning
 
  | Field = Machine learning
| Homepage = www.ecmlpkdd2009.net/
 
| Start date = Sep 7, 2009
 
| End date =  Sep 11, 2009
 
| City= Bled
 
| State =
 
| Country =  Slovenia
 
 
  | 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


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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
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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.