Certainty-based prototype insertion/deletion for classification with metric adaptation.
Lydia J. Fischer, Barbara Hammer, Heiko Wersing · 2015
Abstract. We propose an extension of prototype-based classification models to automatically adjust model complexity, thus offering a powerful technique for online, incremental learning tasks. The incremental technique is based on the notion of the certainty of an observed classification. Unlike previous work, we can incorporate matrix learning into the framework by relying on the cost function of generalised learning vector quantisation (GLVQ) for prototype insertion, deletion, as well as training. In several benchmarks, we demonstrate that the proposed method provides compa-rable results to offline counterparts and an incremental support vector machine, while enabling a better control of the required memory. 1