Evolving class for SVM's incremental learning.

Abstract

International audienceThe good generalization performance of support vector machines (SVM) has made them a popular tool in artificial intelligence community. In this paper, we prove that SVM multi class algorithms are not equivalent for all classification problems we present a new approach for incremental learning using SVM that create a rejection class which would be interesting for fault diagnosis where fault classes usually evolve with time : It is when some new samples may be rejected by all the current classes. Hence, these samples may correspond to a new fault (a new class) which may appear after the first training step