Connectionists: [CfP] SI: Issue: Non-Iterative Approaches and Their Applications -- Cognitive Computation

[filippo bianchi]

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



*Special Issue: Non-Iterative Approaches and Their Applications* on *Cognitive
Computation* (Springer)


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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. 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 issue 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.

Researchers will be invited to submit innovative works or comparative
studies with both iterative and non-iterative methods. Typical paradigms
include (but not limited to) random vector functional link (RVFL), echo
state networks (ESN), extreme learning machines (ELM), kernel ridge
regression (KRR), random forests (RF) and so on.


*Topics covered:*

   - Methods with and without randomization
   - Regression, classification and time series analysis
   - Dimensionality reduction
   - Spectral methods
   - 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.
   - 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

*Journal description*

Cognitive Computation, Springer (https://link.springer.com/journal/12559)
is an international, peer-reviewed, interdisciplinary journal that
publishes cutting-edge articles focusing on original basic and applied work
involving bio-inspired computational accounts of all aspects of natural and
artificial cognitive systems. Cognitive Computation has an Impact Factor of
3.44.

*Important Dates*

   - Submissions Deadline *1st July 2018*
   - First notification of acceptance 1st Nov 2018
   - Submission of revised papers 1st Jan 2019
   - Final notification to the authors 1st Mar 2019
   - Submission of final/camera-ready papers 1st April 2019
   - Publication of special issue 2019




*Paper Submission*



All papers should follow the manuscript preparation requirements for the
Springer Cognitive Computation submissions (see
http://www.springer.com/12559). The authors are requested to submit their
manuscripts via the online submission manuscript system, available at
http://www.editorialmanager.com/cogn/. During the first step of the
submission, authors should select as Article Type “*S.I. Non-Iterative
Approaches and Their Applications*”. For further (technical) questions,
please contact the Editor-in-Chief: ahu at cs.stir.ac.uk.



*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


------------------------------------------------------------
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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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