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

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Link: https://sites.google.com/site/nqdoan/home/special-session
 
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 http://www.ieee.org/conferences_events/conferences/publishing/templates.html for the paper. For more information about submission procedure, please visit: http://www.ijcnn.org/

--- Session chairs ---
Nhat-Quang Doan (University of Science and Technology of Hanoi, Vietnam) - doan-nhat.quang@usth.edu.vn
Hanane Azzag (University of Paris-Nord, France) - hanene.azzag@lipn.univ-paris13.fr
Mustapha Lebbah (University of Paris-Nord, France) - mustapha.lebbah@lipn.univ-paris13.fr

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