CFP: special issue on Support vector machines

Chih-Jen Lin cjlin at csie.ntu.edu.tw
Mon Oct 29 20:42:39 EST 2001


                 CALL FOR PAPERS: special issue on SVM

                               NEUROCOMPUTING

                          An International Journal

    published by Elsevier Science B.V., vol. 42-47, 24 issues, in 2002 

        ISNN 0925-2312, URL: http://www.elsevier.nl/locate/neucom

                  Special Issue on Support Vector Machines

               Paper Submission Deadline: February 28, 2002

Further information: http://www.csie.ntu.edu.tw/~cjlin/svmcfp.html
    
Support  Vector  Machines (SVMs) are currently a very active research area 
within machine learning. Motivated by statistical learning theory 
they have been successfully applied to numerous tasks within data mining, 
computer vision and bioinformatics, for example. SVMs are examples 
of a broader category of learning approaches which utilize the concept 
of kernel substitution, thereby making the task of learning more 
tractable by exploiting an implicit mapping into a high dimensional 
space. SVMs have many appealing properties for machine learning. 
For example, the classic SVM learning task involves quadratic programming: 
there is only one solution and this may be found by using many of the 
efficient algorithms developed in optimization theory. Furthermore, 
recently developed model selection strategies can be applied, so that 
few, if any, learning parameters must be set by the operator. Above 
all, they have been found to work very well in practice. 

The  Neurocomputing  journal  invites  original  contributions for the
forthcoming  special  issue  on  Support  Vector Machines from a broad
range  of  areas.  Some topics relevant to this special issue include,
but are not restricted to:

-- Theoretical foundations, algorithms, and implementations

-- Model selection and hyperparameter tuning

-- Choosing kernels for special situations

-- Probabilistic treatment of SVMs

-- SVM methods for large scale problems

-- Benchmarking SVMs against other methods

-- Feature selection methods for SVMs

--  Key  applications  including,  but  not restricted to data mining,
bioinformatics, text categorization, machine vision, etc.

Please  send  two  hardcopies  of the manuscript before February 28, 2002 to:

V. David Sanchez A., Neurocomputing - Editor in Chief -
Advanced Computational Intelligent Systems
P.O. Box 60130,
Pasadena, CA 91116-6130, U.S.A.

Street address:

1149 Wotkyns Drive
Pasadena, CA 91103, U.S.A.
Fax: +1-626-793-5120
Email: vdavidsanchez at earthlink.net

including  abstract,  keywords,  a cover page containing the title and
author  names,  corresponding author name's complete address including
telephone,  fax,  and  email  address,  and  clear  indication of 
submission to the Special Issue on Support Vector Machines.

Guest Editors

Colin Campbell
Department of Engineering Mathematics
Bristol University, Bristol BS8 1TR
United Kingdom
Phone: (+44) (0) 117 928 9858
Fax: (+44) (0)117-925-1154
Email: C.Campbell at bristol.ac.uk

Chih-Jen Lin
Department of Computer Science and Information Engineering
National Taiwan University
Taipei, Taiwan, 106
Phone: (+886) 2-2362-5336 x 413
Fax: (+886) 2-2362-8167
Email: cjlin at csie.ntu.edu.tw

S. Sathiya Keerthi
Department of Mechanical Engineering
National University of Singapore
10 KentRidge Crescent
Singapore 119260
Republic of Singapore
Phone: (+65) 874-4684
Fax: (+65) 779-1459
Email: mpessk at guppy.mpe.nus.edu.sg

V. David Sanchez A., Neurocomputing - Editor in Chief -
Advanced Computational Intelligent Systems
P.O. Box 60130
Pasadena, CA 91116-6130
U.S.A.
Fax: +1-626-793-5120
Email: vdavidsanchez at earthlink.net

---




More information about the Comp-bio mailing list