Handling concept drifts in incremental learning with support vector machines
Nadeem Ahmed Syed, Huan Liu, Kah Kay Sung · 1999
With the increase in the size of real-world databases, there is an ever-increasing need to scale up inductive learning algorithms.Incremental learning techniques are one possible solution to the scalability problem.In this paper, we propose three ctiteria to evaluate the robustness and reliability of incremental learning methods, and use them to study the robustness of an incremental training method for Support Vector Machines.We provide empirical results using benchmark machine learning datasets to show that support vectors form a svccdnct and suficient set for block-by-block incremental learning.