Constructive training of probabilistic neural networks

This paper presents an easy to use constructive training algorithm for Probabilis tic Neural Networks a special type of Radial Basis Function Networks In contrast to other algorithms prede nition of the network topology is not required The pro posed algorithm introduces new hidden units whenever necessary and adjusts the shape of already existing units individually to minimize the risk of misclassi cation This leads to smaller networks compared to classical PNNs and therefore enables the use of large datasets Using eight classi cation benchmarks from the StatLog project the new algorithm is compared to other state of the art classi cation meth ods It is demonstrated that the proposed algorithm generates Probabilistic Neural Networks that achieve a comparable classi cation performance on these datasets Only two rather uncritical parameters are required to be adjusted manually and there is no danger of overtraining the algorithm clearly indicates the end of training In addition the networks generated are small due to the lack of redundant neurons in the hidden layer