Dear Colleagues:
We announce a new paper:
A Note on the Decomposition Methods for Support Vector Regression
by
Shuo-Peng Liao, Hsuan-Tien Lin, and Chih-Jen Lin
http://www.csie.ntu.edu.tw/~cjlin/papers/svr2and4.ps.gz
Abstract:
The dual formulation of support vector regression involves with
two closely related sets of variables. When the decomposition
method is used, many existing approaches use pairs of
indices from these two sets as the working set.Basically they
select a base set first and then expand it so that all
indices are pairs. This makes the implementation different
from that for support vector classification. In addition,
a larger optimization sub-problem has to be solved in each iteration.
In this paper from different aspects we demonstrate that
there are no needs to do so. In particular we show that
directly using this base set as the working set
leads to similar convergence (number of iterations).
Therefore, not only the program can be simpler,with a smaller
working set and similar number of iterations, it can also be
more efficient.
Any comments are very welcome.
Sincerely,
Chih-Jen Lin
Dept. of Computer Science
National Taiwan Univ.
|