Advances in Learning with Kernels@ESANN 2016 : Special session - Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints
Call For Papers
[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
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
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.
Paper submission deadline : 27 November 2015
Notification of acceptance : 31 January 2016
The ESANN 2016 conference : 27-29 April 2016
SPECIAL SESSION ORGANISERS
Luca Oneto, Davide Anguita, University of Genoa (Italy),
Fabio Aiolli, Michele Donini, Nicolò Navarin, University of Padua (Italy)