Online incremental supervised growing neural gas
Felipe Duque-Belfort, Hansenclever F. Bassani, A.F.R. Araujo · 2017
Online learning algorithms are intrinsically designed to deal with large amounts of data because of the one-instance-at-a-time approach to the learning process, circumventing memory issues and enabling real time learning. However, most online algorithms require previous knowledge of the problem to predetermine the number of categories to be learned, or some other kind of meta-information that is not likely to be available to a generic system. In this work, an online, incremental algorithm, oiSGNG, is proposed, whose main features are: zero nodes initialization and the original batch SGNG node insertion mechanism [10]. The results improved on the state of the art in 5 out of 12 multiclass datasets.