An Adaptive Normalized Gaussian Network and Its Application to Online Category Learning

In online applications, where training samples sequentially arise during execution, incremental learning schemes have to be applied. In this paper we propose an adaptive Normalized Gaussian Network model (NGnet) suitable for incremental learning. Following a statistical account we present a truly sequential training procedure. Key to the learning algorithm are local unit manipulation mechanisms for network growth and pruning which continuously adapt the network's complexity according to task demands. We evaluate our model in artificial and real-world categorization tasks. Thereby, we additionally introduce a framework for the categorization on adaptive feature spaces. In the system, a simultaneous extraction of class-discriminative features facilitates the NGnet's categorization of input patterns. We present simulation results which demonstrate that the framework realizes a rapid learning from few examples, small-sized network models, and an improved generalization ability. A comparison to incremental support vector machine classification yields a favorable performance of our model.