Connectionists: special session on Learning and Modelling Big Data

[Barbara Hammer]

*** Apologies for cross posting ***

Special Session on Learning and Modeling Big Data

at ESANN 2014, 23-25 April 2014, Bruges, Belgium, http://www.esann.org

Organizers: Barbara Hammer (Bielefeld University, DE), Haibo He (Rhode 
Island, USA), Thomas Martinetz (University of Luebeck, DE)

Abstract:
Big data in the sense of large or streaming data sets, very high 
dimensionality, or complex data formats constitute one of the major 
challenges faced by machine learning today, caused by powerful sensors 
and digitalization techniques as well as dramatically increased storage 
capabilities. In this realm, a couple of typical assumptions of machine 
learning can no longer be met, causing the need for novel algorithmic 
developments and paradigm shifts, such as

* online learning and techniques for streaming data
* learning from non i.i.d. data and skewed distributions
* life-long adaptation of model complexity and hyper-parameters
* linear or sublinear algorithms with limited online memory capacity
* parallel implementations
* sparse representation of data, efficient information compression
* interpretable models
* learning from the crowd
* good priors in the context of extremely high dimensionality

We solicit contributions focussing on novel algorithmic developments, 
theoretical investigations or applications connected to this 
non-exhaustive list of topics.

Schedule:
Paper submission deadline : 29 November 2013
Notification of acceptance : 31 January 2014
Deadline for final papers : 21 February 2014
ESANN 2014 conference : 23-25 April 2014

Infos on submissions can be found at http://www.esann.org

-- 
Prof. Dr. Barbara Hammer
CITEC centre of excellence
Bielefeld University
D-33594 Bielefeld
Phone: +49 521 / 106 12115
Fax:   +49 521 / 106 12181