[Comp-neuro] Special Issue of Applied Soft Computing on Non-iterative Approaches in Learning

Teng Teck Hou dengdehao at gmail.com
Fri Jul 8 07:16:38 CEST 2016

Special Issue of Applied Soft Computing (Elsevier) (Impact Factor: 2.8)


Submission is open now


Special Issue on Non-iterative Approaches in Learning (Includes comparisons
with iterative methods)


Call for Papers




Optimization, which plays a central role in learning, has received
considerable attention from academics, researchers, and domain workers. Many
optimization problems in machine learning are solved by iterative methods
which generate a sequence of improving approximated solutions with some
termination criteria. These methods usually suffer from low convergence rate
and are sensitive to parameter settings (such as learning rate/step size,
maximum number of iterations). On the other hand, non-iterative solutions,
which are usually presented in closed-form manner, are in general
computationally faster than iterative solutions. However, comparative
studies with iterative methods are also welcome. 


The main focus of this special issue is to present the recent advances in
non-iterative solutions in learning. Original contributions and surveys are
welcome. The special issue aims to promote non-iterative concepts in the
field of learning. 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 issue aims to
bridge this gap. Besides the dissemination of the latest research results on
non-iterative algorithms, it is also expected that this special issue will
cover some industrial applications, present some new ideas and identify
directions for future studies. The topics of the special issue include, but
are not limited to:


* Methods with and without randomization

* Regression, classification and time series

* Kernel methods such as kernel ridge regression, kernel adaptive filters,

* 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


Submission format and Guidelines

Papers will be evaluated based on their originality, presentation, relevance
and contribution to the development of non-iterative methods, as well as
their suitability and the quality in terms of both technical contribution
and writing. The submitted papers must be written in good English and
describe original research which has not been published nor is currently
under review by other journals or conferences. If used, the previously
published conference papers should be clearly identified by the authors (at
the submission stage) and an explanation should be provided how such papers
have been extended to be considered for this special issue. Guest Editors
will make an initial determination on the suitability and scope of all
submissions. Papers that either lack originality, clarity in presentation or
fall outside the scope of the special issue will not be sent for review and
the authors will be promptly informed of such cases. Author guidelines for
preparation of manuscript can be found at
http://www.journals.elsevier.com/applied-soft-computing/  Manuscripts should
be submitted online at: http://ees.elsevier.com/asoc/  


Applied Soft Computing Journal is well indexed. Its impact factors are 2.8
(2 years) and 3.2 (5 years).


Important dates 

Manuscript submission:        15th Aug 2016

Revised version submission:   31st Jan 2017

Acceptance notification:      31st March 2017

Expected Publication:         Mid 2017       


Guest Editors

Dr P N Suganthan, Nanyang Technological University, Singapore.
epnsugan at ntu.edu.sg  


Prof. Sushmita Mitra, Indian Statistical Institute, India.
sushmita at isical.ac.in  


Dr Ivan Tyukin, Department of Mathematics, University of Leicester, UK.
I.Tyukin at le.ac.uk

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