Automatic Model Selection in a Hybrid Perceptron/Radial Network
We introduce an algorithm for incrementaly constructing a hybrid network fo radial and perceptron hidden units. The algorithm determins if a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determins the number of hidden units. This results in a final architecture which is often much smaller than an RBF network or a MLP. A benchmark on four classification problems and three regression problems is given. The most striking performance improvement is achieved on the vowel data set .
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