Connectionists: CFP for ESANN17 Special Session on Algorithmic Challenges in Big Data Analytics

[Veronica Bolon Canedo]

[Apologies if you receive multiple copies of this CFP]

Call for papers:  special session on "Algorithmic Challenges in Big Data Analytics" at ESANN 2017

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) 
26-28 April 2017, Bruges (Belgium) -  <http://www.esann.org/>http://www.esann.org <http://www.esann.org/>
Algorithmic Challenges in Big Data Analytics

Organized by: Veronica Bolon-Canedo, Amparo Alonso-Betanzos (University of A Coruña, Spain), Beatriz Remeseiro (University of Barcelona, Spain), David Martinez-Rego (University College London, UK), Konstantinos Sechidis (University of Manchester, UK)


In the past few years, the advent of Big Data has brought unprecedented challenges to machine learning researchers. Dealing with huge volumes of data, both in terms of instances and features, makes the learning task more complex and computationally demanding than ever.

Processing these massive datasets is key to providing a wealth of information, but at the same time is a challenge for machine learning researchers, who see how classic algorithms are now useless. The community expects new methods that not only allow accurate analysis of the available data, but which are also robust and scalable when dataset sizes increase. In other words, the challenge now is to find “good enough” solutions as “fast” as possible and as “efficiently” as possible. This issue becomes critical in situations in which there exist temporal or spatial constraints like real-time applications or unapproachable computational problems requiring learning.

We invite papers aiming to examine the recent progress in the field, together with new open challenges derived from the increased data availability. In particular, topics of interest include, but are not limited to:

Pre-processing, processing and post-processing of Big Data.
Methods, algorithms and theory for Big Data analytics.
Recent advances and challenges in machine learning for Big Data.
Distributed learning in the context of Big Data.
Deep learning with massive-scale datasets.
Applications: healthcare, social media, bioinformatics, genomics, finance, surveillance, etc.


Submitted papers will be reviewed according to the ESANN reviewing process and will be evaluated on their scientific value: originality, correctness, and writing style.

IMPORTANT DATES:

Paper submission deadline : 19 November 2016
Notification of acceptance : 31 January 2017
ESANN conference : 26-28 April 2017
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