Connectionists: (Imapct Factor - 2.8) Journal Special Issue on RVFL, ELM, RF, KRR, and others

[Ponnuthurai Nagaratnam Suganthan]

Extended Final Deadline


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

Special Issue on Non-iterative Approaches in Learning (Includes comparisons with iterative methods)
Call for Papers
http://www.journals.elsevier.com/applied-soft-computing/call-for-papers/special-issue-on-non-iterative-approaches-in-learning-includ/

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, 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

Typical paradigms include random vector functional link (RVFL), extreme learning machines (ELM),
kernel ridge regression (KRR), random forests (RF), etc.

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:          30th Sept 2016 (Final Extended Deadline)
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


________________________________
CONFIDENTIALITY: This email is intended solely for the person(s) named and may be confidential and/or privileged. If you are not the intended recipient, please delete it, notify us and do not copy, use, or disclose its contents.
Towards a sustainable earth: Print only when necessary. Thank you.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20160819/e4f5777c/attachment.html>