Incremental learning for visual classification using Neural Gas
Ignazio Aleo, Paolo Arena, Luca Patané · 2010
In this paper we investigate a novel algorithm for solving classification problems in an action-oriented perception framework supported by visual feedback. The approach is based on an extension of the Neural Gas with local Principal Component Analysis (NGPCA) algorithm. As an abstract Recurrent Neural Network (RNN) this model is able to complete a partially given pattern. Under this point of view it is possible to generalize the model as a supervised classifier in which for a given segmented object (i.e. with particular visual cues) the class variable is retrieved as the network outputs. An incremental version of the algorithm is also presented and applied in a robotic platform for object manipulation tasks.