posted by user: nqdoan || 1252 views || tracked by 3 users: [display]

SML-DS 2015 : Scalable Machine Learning for Data Stream - Special Session in IJCNN 2015


When Jul 12, 2015 - Jul 17, 2015
Where Killarney, Ireland
Submission Deadline Jan 15, 2015
Notification Due Mar 15, 2015
Final Version Due Apr 15, 2015
Categories    data stream   neural network   visualization

Call For Papers

--- Objectives ---
In many fields, such as multimedia, insurance information systems, bio-bioinformatics, and with advances in data collection and storage technologies have allowed companies to accumulate and to acquire vast amounts of data (Terabyte, Petabyte, and sometimes Zettabyte). In many cases the data may arrive very rapidly in streaming. A data stream is often presented as an ordered sequence of data that in many applications can be read only once or a small number of times using limited computing and storage capabilities. Recent trends in hardware have brought new challenges to the programming and machine learning community and multi-core systems. Data explosion involves that machine learning algorithms are adapted using the new parallelism paradigm as “MapReduce”. Somme researches have proposed incremental, collaborative and online learning methods making it possible to deal with massive data (big data). This requires a process capable of dealing data continuously with restrictions of memory and time.

This special session offers a meeting opportunity for academic and industry researchers in the fields of machine learning, neural network, data visualization, and Big Data to discuss new areas of learning methods and experimental design. We encourage researchers and practitioners to submit papers describing original research addressing data stream and scalable machine learning challenges.

--- Topics ---
This includes but is not restricted to the following topics:

● Clustering, classification from data streams
● Neural networks approaches
● Online learning
● Method of detecting changes in evolving data
● Applications of detecting changes of evolving data
● Clustering and classification of data of changing distributions.
● Visualization of data streams and stream mining results.
● Theoretical frameworks for stream mining.
● Scalability of data stream mining systems
● Interactive stream mining techniques
● Distributed ensemble classifier
● Distributed neural networks
● Parallel and distributed computational intelligence
● Future research challenges of data stream mining
● Deep learning

--- Guidelines for authors ---
Please use IEEE template for the paper. For more information about submission procedure, please visit:

--- Session chairs ---
Nhat-Quang Doan (University of Science and Technology of Hanoi, Vietnam) -
Hanane Azzag (University of Paris-Nord, France) -
Mustapha Lebbah (University of Paris-Nord, France) -

Related Resources

ICML 2017   34th International Conference on Machine Learning
WSCG 2017   WSCG - 25. Conference on Computer Graphics, Visualization and Computer Vision
MLDM 2017   Machine Learning and Data Mining in Pattern Recognition
JWS-VOILA 2016   Special Issue on Visualization and Interaction for Ontologies and Linked Data
IJCAI 2017   International Joint Conference on Artificial Intelligence
IJDKP 2016   International Journal of Data Mining & Knowledge Management Process
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
SI-VGA 2016   Special Issue on Visual Game Analytics - Call for Papers
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
CCML 2017   The 16th China Conference on Machine Learning