An improved training algorithm for support vector machines

We investigate the problem of training a Support Vector Machine (SVM) [1, 2, 7] on a very large date base (e.g. 50,000 data points) in the case in which the number of support vectors is also very large (e.g. 40,000). Training a SVM is equivalent to solving a linearly constrained quadratic programming (QP) problem in a numberofvariables equal to the number of data points. This optimization problem is known to be challenging when the number of data points exceeds few thousands. In previous work, done byusaswell as by other researchers, the strategy used to solve the large scale QP problem takes advantage of the fact that the expected number of support vectors is small (< 3; 000). Therefore, the existing…