Approximation and Classification in Medicine with IncNet Neural Networks1
Norbert Jankowski · 1999
Structure of incremental neural network (IncNet) is controlled by growing and pruning to match the complexity of training data. Extended Kalman Filter algorithm and its fast version is used as learning algorithm. Bi-central transfer functions, more flexible than other functions commonly used in artificial neural networks, are used. The latest improvement added is the ability to rotate the contours of constant values of transfer functions in multidimensional spaces with only N - 1 adaptive parameters. Results on approximation benchmarks and on the real world psychometric classification problem clearly shows superior generalization performance of presented network comparing with other classification models.