Applying machine learning to big data streams : An overview of challenges
Christoph Augenstein , Norman Spangenberg , Bogdan Franczyk
AbstractThe importance of processing stream data increases with new technologies and new use cases. Applying machine learning to stream data and process them in real time leads to challenges in different ways. Model changes, concept drift or insufficient time to train models are a few examples. We illustrate big data characteristics and machine learning techniques derived from literature and conclude with available approaches and drawbacks
|Publication size in sheets||0.5|
|Book||4th International Conference on Soft Computing and Machine Intelligence (ISCMI), 2017, Institute of Electrical and Electronics Engineers, ISBN 9781538613146, , 205 p.|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.