Exploiting Sampling and Meta-learning for Parameter Setting for Support Vector Machines
Petr Kuba, Pavel Brazdil, Carlos Soares, Adam Woźnica · 2002
It is a known fact that good parameter settings affect the performance of many machine learning algorithms. Support Vector Machines (SVM) and Neural Networks are particularly affected. In this paper, we concentrate on SVM and discuss some ways to set its parameters. The first approach uses small samples, while the second one exploits meta-learning and past results. Both methods have been thoroughly evaluated. We show that both approaches enable us to obtain quite good results with significant savings in experimentation time.