This paper presents i-AA1*, a constructive, incremental learning algorithm for a special class of weightless, self-organizing networks. In i-AA1*, learning consists of adapting the nodes’ functions and the network’s overall topology as each new training pattern is presented. Provided the training data is consistent, computational complexity is low and prior factual knowledge may be used to “prime” the network and improve its predictive accuracy. Empirical generalization results on both toy problems and more realistic tasks demonstrate promise.
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