Simpler core vector machines with enclosing balls

The core vector machine (CVM) is a recent approach for scaling up kernel methods based on the notion of minimum enclosing ball (MEB). Though conceptually simple, an efficient implementation still requires a sophisticated numerical solver. In this paper, we introduce the enclosing ball (EB) problem where the ball's radius is fixed and thus does not have to be minimized. We develop efficient (1 + )-approximation algorithms that are simple to implement and do not require any numerical solver. For the Gaussian kernel in particular, a suitable choice of this (fixed) radius is easy to determine, and the center obtained from the (1 + )-approximation of this EB problem is close to the center of the corresponding MEB. Experimental results show that the proposed algorithm has accuracies comparable to the other large-scale SVM implementations, but can handle very large data sets and is even faster than the CVM in general.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: András Kocsor, Ivor W. Tsang, James T. Kwok
• Keywords: ICML 2007, Large-scale Svm Implementations, Machine Learning, Minimum Enclosing Ball, Sophisticated Numerical Solver