Connectionists: [Deadline extended] Special Session on Non-Iterative Approaches in Learning (WCCI 2018)

[filippo bianchi]

*********** Call for Papers ***********

*Special session on Non-iterative Approaches in Learning (Including
comparative studies with iterative methods)*

2018 IEEE World Congress on Computational Intelligence (WCCI 2018)
Rio de Janeiro, BRAZIL, 08-13 July 2018 - http://www.ecomp.poli.
br/~wcci2018/
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*DESCRIPTION:*
Optimization, which plays a central role in learning, has received
considerable attention from academics, researchers, and domain workers.
Many optimization problems in machine learning can be tackled with
non-iterative approaches, which can be presented in closed-form manner.
Those methods are in general computationally faster than iterative
solutions and less sensitive to parameter settings. Even though
non-iterative methods have attracted much attention in recent years, there
exists a performance gap when compared with older methods and other
competing paradigms. This special session aims to bridge this gap.

The first target of this special session is to present the recent advances
of non-iterative solutions in learning. Secondly, the focus is on promoting
the concepts of non-iterative optimization with respect to counterparts,
such as gradient-based methods and derivative-free iterative optimization
techniques. Besides the dissemination of the latest research results on
non-iterative algorithms, it is also expected that this special session
will cover some practical applications, present some new ideas and identify
directions for future studies.

Original contributions, comparative studies with both iterative and
non-iterative methods are welcome. Typical paradigms include (but not
limited to) random vector functional link (RVFL), Echo State Networks
(ESN), kernel ridge regression (KRR), random forests (RF), etc…

The topics of the special session include, but are not limited to:

   - Methods with and without randomization
   - Regression, classification and time series analysis
   - Kernel methods such as kernel ridge regression, kernel adaptive
   filters, etc.
   - Feedforward, recurrent, multilayer, deep and other structures.
   - Ensemble learning
   - Moore-Penrose pseudo inverse, SVD and other solution procedures.
   - Gaussian Process regression
   - Non-iterative methods for large-scale problems with and without kernels
   - Theoretical analysis of non-iterative methods
   - Comparative studies with competing iterative methods
   - Applications of non-iterative solutions in domains such as power
   systems, biomedical, finance, signal processing, big data and all other
   areas

*Important Dates*

   - *1**st** February 2018* – paper submission deadline
   - 15th March 2018 – Paper acceptance notification
   - 8-13 July 2018 – IEEE WCCI 2018 conference, Rio de Janeiro, Brazil

*Paper Submission*

Papers submitted to this Special Session are reviewed according to the same
rules as the submissions to the regular sessions of WCCI 2018.

Authors who submit papers to this session must follow this link (
http://ieee-cis.org/conferences/ijcnn2018/upload.php​) and select in *"**Main
Research topic**"* the option *"**S1. Special Session on Non-iterative
Approaches in Learning**"* from the drop-down menu.

Submissions to regular and special sessions follow identical format,
instructions, deadlines and procedures of the other papers.

*Journal Extension*

The best papers submitted to the Special Session will be invited for a
journal extension within the Special Issue “*Non-Iterative Learning
Approaches and Their Applications*” on the journal Cognitive Computation,
Springer (https://link.springer.com/journal/12559).

Cognitive Computation (impact factor 3.44) is an international,
peer-reviewed, interdisciplinary journal that publishes cutting-edge
articles describing original basic and applied work involving bio-inspired
computational accounts of all aspects of natural and artificial cognitive
systems.​

*Organizers*


   - Dr P. N. Suganthan, Nanyang Technological University, Singapore.
   epnsugan at ntu.edu.sg
   - Dr. Filippo Maria Bianchi, UiT the Arctic University of Norway,
   Tromsø, filippo.m.bianchi at uit.no




Link to PDF version of CfP:
https://drive.google.com/open?id=1LEu22zA2XHXcADEwbedsTmAAW_uSn56l



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Filippo Maria Bianchi, PhD.



*Postdoctoral fellow at Machine Learning group,Department of Physics and
Technology,UiT The Arctic University of
Norway.Homepage: https://sites.google.com/view/filippombianchi/home
<https://sites.google.com/view/filippombianchi/home>*
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