The workshop on Classifier Learning from Difficult Data (CLD 2019)

Jun 12 - 14, 2019, Faro, Algarve, Portugal

Scope & Topics

Nowadays many practical decision task require to build models on the basis of data which included serious difficulties, as imbalanced class distributions, high number of classes, high-dimensional feature, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few. Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods which can combat the mentioned above difficulties should be the focus of intense research. The main aim of this workshop is to discuss the problems of data difficulties, to identify new issues, and to shape future directions for research.

Topics of Interest :

• Learning from imbalanced data
  
• learning from data streams, including concept drift management
  
• learning with limited ground truth access
  
• learning from high dimensional data
  
• learning with a high number of classes
  
• learning from massive data, including instance and prototype selection
  
• learning on the basis of limited data sets, including one-shot learning
  
• learning from incomplete data
  
• case studies and real-world applications
  

Important Dates

Submission Deadline	:	December 15, 2018
Authors Notification	:	February 15, 2019
Final Manuscript Due	:	April 05, 2019

 

User Name : austin
Posted 04-12-2018 on 15:02:27 AEDT

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