Incremental PSVM for underwater target classification with incorporation of new classes

Abstract

This paper describes a novel incremental PSVM to incorporate new target class information unavailable previously in the underwater target classification system. It is capable of updating already existing multiclass `One against Rest' Proximal Support Vector Machine classifier on arrival of features of new classes. The performance of the algorithm is studied on real data. Simulation establishes the effectiveness of the algorithm in adding samples of new classes or of existing classes into the training set incrementally without much affecting the storage space and computation.