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<div id="divRpF727118" style="direction:ltr"><font face="Tahoma" color="#000000" size="2"></font><font size="4">Extended Final Deadline</font><br>
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Special Issue of Applied Soft Computing (Elsevier) (Impact Factor: 2.8)<br>
<br>
Special Issue on Non-iterative Approaches in Learning (Includes comparisons with iterative methods)<br>
Call for Papers<br>
http://www.journals.elsevier.com/applied-soft-computing/call-for-papers/special-issue-on-non-iterative-approaches-in-learning-includ/
<br>
<br>
Optimization, which plays a central role in learning, has received considerable attention from academics,
<br>
researchers, and domain workers. Many optimization problems in machine learning are solved by iterative
<br>
methods which generate a sequence of improving approximated solutions with some termination criteria.
<br>
These methods usually suffer from low convergence rate and are sensitive to parameter settings (such as
<br>
learning rate/step size, maximum number of iterations). On the other hand, non-iterative solutions,
<br>
which are usually presented in closed-form manner, are in general computationally faster than iterative
<br>
solutions. However, comparative studies with iterative methods are also welcome. <br>
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The main focus of this special issue is to present the recent advances in non-iterative solutions in
<br>
learning. Original contributions and surveys are welcome. The special issue aims to promote non-iterative
<br>
concepts in the field of learning. Even though non-iterative methods have attracted much attention in recent
<br>
years, there exists a performance gap when compared with older methods and other competing paradigms.
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This special issue aims to bridge this gap. Besides the dissemination of the latest research results on
<br>
non-iterative algorithms, it is also expected that this special issue will cover some industrial
<br>
applications, present some new ideas and identify directions for future studies. The topics of the
<br>
special issue include, but are not limited to:<br>
<br>
* Methods with and without randomization<br>
* Regression, classification and time series<br>
* Kernel methods such as kernel ridge regression, kernel adaptive filters, etc.<br>
* Feedforward, recurrent, multilayer, deep and other structures.<br>
* Ensemble learning<br>
* Moore-Penrose pseudo inverse, SVD and other solution procedures. <br>
* Non-iterative methods for large-scale problems with and without kernels<br>
* Theoretical analysis of non-iterative methods<br>
* Comparative studies with competing iterative methods<br>
* Applications of non-iterative solutions in domains such as power systems, biomedical, finance, signal
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processing, big data and all other areas<br>
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Typical paradigms include random vector functional link (RVFL), extreme learning machines (ELM),
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kernel ridge regression (KRR), random forests (RF), etc. <br>
<br>
Submission format and Guidelines<br>
Papers will be evaluated based on their originality, presentation, relevance and contribution to the
<br>
development of non-iterative methods, as well as their suitability and the quality in terms of both
<br>
technical contribution and writing. The submitted papers must be written in good English and describe
<br>
original research which has not been published nor is currently under review by other journals or
<br>
conferences. If used, the previously published conference papers should be clearly identified by
<br>
the authors (at the submission stage) and an explanation should be provided how such papers have been
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extended to be considered for this special issue. Guest Editors will make an initial determination on
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the suitability and scope of all submissions. Papers that either lack originality, clarity in presentation
<br>
or fall outside the scope of the special issue will not be sent for review and the authors will be promptly
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informed of such cases. Author guidelines for preparation of manuscript can be found
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at http://www.journals.elsevier.com/applied-soft-computing/ Manuscripts should be submitted
<br>
online at: http://ees.elsevier.com/asoc/ <br>
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Applied Soft Computing Journal is well indexed. Its impact factors are 2.8 (2 years) and 3.2 (5 years).<br>
<br>
Important dates <br>
Manuscript submission: 30th Sept 2016 (Final Extended Deadline)<br>
Revised version submission: 31st Jan 2017<br>
Acceptance notification: 31st March 2017<br>
Expected Publication: Mid 2017 <br>
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Guest Editors<br>
Dr P N Suganthan, Nanyang Technological University, Singapore. epnsugan@ntu.edu.sg
<br>
Prof. Sushmita Mitra, Indian Statistical Institute, India. sushmita@isical.ac.in
<br>
Dr Ivan Tyukin, Department of Mathematics, University of Leicester, UK. I.Tyukin@le.ac.uk
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