Learning SVM from Distributed, Non-Linearly Separable Datasets with Kernel Methods

Learning from distributed data sets is common problem nowadays and the question of its actuality can be inferred by the number of applications and from even higher number of problems coming from real world business solutions. Here we will review the question of distributed classification with Support Vector Machines, and present our approach to handle the problem in effective way.