Chih-Jen Lin,
Department of Computer Science, National Taiwan University.
S. Sathiya Keerthi,
Department of Mechanical Engineering,
National University of Singapore.
Call for papers: deadline May 31.
Please email
Chih-Jen Lin
your paper following the submission guideline of
ICONIP 2002.
Description
Support Vector Machines (SVMs) and related kernel methods
are currently very active research areas within neural
computation and 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 such as solving convex quadratic programming problems
and they have been found to work very well in practice.
The aim of the special session is to present new perspectives and new directions in SVM and kernel methods. We seek contributions from different aspects of this topic: theory, implementations, new methodologies, and applications.