[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*
<https://outlook.manchester.ac.uk/owa/redir.aspx?SURL=PGDkTcEIJeULeV7oQeYqIKmtHW_t-QAO0K6RN-VFEHuaXGT8JdvTCGgAdAB0AHAAOgAvAC8AdwB3AHcALgBlAHMAYQBuAG4ALgBvAHIAZwA.&URL=http%3a%2f%2fwww.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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