M. Clausel is co-organizing a special session at the IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016) held at in Montreal, Canada; October 17-19, 2016.
|Massih-Reza Amini||Marianne Clausel||Eric Gaussier|
|University of Grenoble, France||University of Grenoble, France||University of Grenoble, France|
Aims and Scope
Huge amounts of data are now easily and legally available on the Web. This data is generally heterogeneous and merely structured. Data mining and Machine learning models which have been developed to automatically retrieve, classify or cluster observations on large yet homogeneous data collections have to be rethought. Indeed, many challenging problems, inevitably associated to Big Data, have manifested the needs for tradeoffs between the two conflicting goals of speed and accuracy. This has led to some recent initiatives in both theory and practice from different communities as machine learning, data mining and statsitics. The goal of this special session is to bring together research studies aiming at developing new data mining and machine learning tools to handle new challenges associated to data science.
Topics of Interest:
- Distributed on-line learning
- Multi-task learning for big data
- Transfer Learning for big data
- Optimization techniques for large-scale learning
- Handling large number of target classes in big data
- Structured prediction models in big data
- Speed/Accuracy tradeoffs in big data
- Statistical inference for big data
- Noise in Big data