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Workshop on Scalable Machine Learning
Co-located with the INNS Big Data conference
Thessaloniki, Greece, 23th-25th October 2016
http://sml2016.sciencesconf.org/
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Important Dates
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-Paper submission: Mai 9th, 2016
-Notification of paper acceptance: June 6th, 2016
-Camera-ready submission (CCIS): June 13th, 2016

Overview
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Scalable Machine Learning occurs when Statistics, Systems, Machine Learning and Data Mining are combined into flexible, often non-parametric, and scalable techniques for analyzing large amounts of data at internet scale. In many fields, such as multimedia, insurance information systems, bio-informatics, 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). Furthermore, proprietary data sources may be enriched and merged with other heterogeneous sources developed by other organisations including government data named open data. In this context, it appears very important to use techniques and technologies of “Big Data” (such as MapReduce, cloud, parallel and streaming algorithms, etc.) in addition to rethink our algorithms to make them scalable.

This workshop offers a meeting opportunity for academic and industry researchers in the fields of machine learning, neural network, data visualization, data mining 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 complex data and scalable machine learning challenges.

This includes but is not restricted to the following topics:

-Clustering and classification for Big Data
-Neural networks approaches
-Distributed neural networks
-Deep learning
-Online learning for high-velocity streaming data
-Methods of detecting changes in evolving data
-Clustering and classification of data of changing distributions
-Visualization of big data and data streams
-Theoretical frameworks for big data mining
-Scalable algorithms for big data
-Interactive stream mining techniques
-Distributed ensemble classifier
-Collaborative methods
-Parallel and distributed computational intelligence
-Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)
-Future research challenges of data stream mining

The Workshop is supported by the INNS, and the group of "Data Mining et Apprentissage" of Société Française de Statistique (French Statistical Society).

Paper Submission and Publication
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Original works submitted as a regular paper limited to a maximum of 14 pages in Springer format will be published in the proceedings to be available electronically as a Springer book in ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING Series, to download for delegates.

Papers submitted to this workshop should use easychair link : https://easychair.org/conferences/?conf=sml20160

It will be peer-reviewed by at least three PC members on the basis of technical quality, relevance, originality, significance and clarity.

At least one author of an accepted submission to the conference should register with a regular fee to present their work at the conference.