Connectionists: SS on Kernel & ML @ ESANN 2016

[Luca Oneto]

[Apologies if you receive multiple copies of this CFP]

Call for papers: special session on "Advances in Learning with Kernels:
Theory and Practice in a World of growing Constraints" at ESANN 2016

European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2016).
27-29 April 2016, Bruges, Belgium - http://www.esann.org

DESCRIPTION:
Kernel methods consistently outperformed previous generations of learning
techniques. They provide a flexible and expressive learning framework that
has been successfully applied to a wide range of real world problems but,
recently, novel algorithms, such as Deep Neural Networks and Ensemble
Methods, have increased their competitiveness against them.
Due to the current data growth in size, heterogeneity and structure, the
new generation of algorithms are expected to solve increasingly challenging
problems. This must be done under growing constraints such as computational
resources, memory budget and energy consumption. For these reasons, new
ideas have to come up in the field of kernel learning, such as deeper
kernels and novel algorithms, to fill the gap that now exists with the most
recent learning paradigms.
The purpose of this special session is to highlight recent advances in
learning with kernels. In particular, this session welcomes contributions
toward the solution of the weaknesses (e.g. scalability, computational
efficiency and too shallow kernels) and the improvement of the strengths
(e.g. the ability of dealing with structural data) of the state of the art
kernel methods. We also encourage the submission of new theoretical results
in the Statistical Learning Theory framework and innovative solutions to
real world problems.
In particular, topics of interest include, but are not limited to:
- Budget (time, memory, energy) Learning
- Structured input and output (e.g. graph/tree kernels)
- Structural Features and Sparse Feature Spaces
- Feature learning, weighting and ranking
- Large Scale Kernel Methods
- Statistical analysis and generalization bounds
- Multiple Kernel Learning
- Mixed Hard/Soft Constraints
- Kernel complexity
- Deeper Kernels
- Novel Kernelized Algorithms (e.g. online learning, preference learning)
- Applications to relevant Real-World Problems

SUBMISSION:
Prospective authors must submit their paper through the ESANN portal
following the instructions provided in
http://www.elen.ucl.ac.be/esann/index.php?pg=submission.  Each paper will
undergo a peer reviewing process for its acceptance. Authors should send as
soon as possible an e-mail with the tentative title of their contribution
to the special session organisers.

IMPORTANT DATES:
Paper submission deadline : 20 November 2015
Notification of acceptance : 31 January 2016
The ESANN 2016 conference : 27-29 April 2016

SPECIAL SESSION ORGANISERS
Luca Oneto <luca.oneto at unige.it>, Davide Anguita <davide.anguita at unige.it>,
University of Genoa (Italy),
Fabio Aiolli, <aiolli at math.unipd.it> Michele Donini <mdonini at math.unipd.it>
, Nicolò Navarin <nnavarin at math.unipd.it>, University of Padua (Italy)


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Luca Oneto, PhD                            University of Genoa
web: www.lucaoneto.com               DITEN Department
e-mail: Luca.Oneto at unige.it          SmartLab Laboratory
e-mail: Luca.Oneto at gmail.com     Via Opera Pia 11a
Fax: +39-010-3532897                   16145 Genoa ITALY
Phone: +39-010-3532192               www.smartlab.ws
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