Automatic Model Selection in a Hybrid Perceptron/Radial Network
- Cite this paper as:
- Cohen S., Intrator N. (2001) Automatic Model Selection in a Hybrid Perceptron/Radial Network. In: Kittler J., Roli F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg
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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