Special Issue on Support Vector Machines

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 scope 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.

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@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 a submission to the Special Issue on Support Vector Machines.