# Core vector regression for very large regression
problems

###
Ivor W. Tsang, James T. Kwok, Kimo T. Lai

**Abstract:**
In this paper, we extend the recently proposed Core Vector
Machine algorithm to the regression setting by generalizing the
underlying minimum enclosing ball problem. The resultant Core
Vector Regression (CVR) algorithm can be used with any
linear/nonlinear kernels and can obtain provably approximately
optimal solutions. Its asymptotic time complexity is linear in the number of
training patterns $m$, while its space complexity is independent
of $m$. Experiments show that CVR has comparable
performance with SVR, but is much faster and produces much fewer
support vectors on very large data sets. It is also
successfully applied to large 3D point sets in computer graphics
for the modeling of implicit surfaces.
*Proceedings of the Twenty-Second International Conference on Machine Learning
(ICML-2005)*, Bonn, Germany, August 2005.

Postscript:
http://www.cs.ust.hk/~jamesk/papers/icml05.ps.gz

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