Recursive SVM based on TEDA

Kangin, Dmitry and Angelov, Plamen (2015) Recursive SVM based on TEDA. In: Statistical learning and data sciences : Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Lecture Notes in Computer Science . Springer, Cham, pp. 156-168. ISBN 9783319170909

Abstract

The new method for incremental learning of SVM model incorporating recently proposed TEDA approach is proposed. The method updates the widely renowned incremental SVM approach, as well as introduces new TEDA and RDE kernels which are learnable and capable of adaptation to data. The slack variables are also adaptive and depend on each point’s ‘importance’ combining the outliers detection with SVM slack variables to deal with misclassifications. Some suggestions on the evolving systems based on SVM are also provided. The examples of image recognition are provided to give a ‘proof of concept’ for the method.