CLASSIFICATION AND PREDICTION - Universität Wien

For a network with … input units, † hidden units, and 1 output, there are ‡‰ˆ
Peter Brezany Institut für Softwarewissenschaft, WS 2002 20
Heuristics for Using Neural Networks (2)
2. The size of the training set
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The training set must be sufficiently large to cover the ranges of inputs available for each
feature. In addition, we want several training examples for each weight in the network.
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weights in the network. We want at least 5 to 10 examples in the training set for each weight.
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3. The learning rate
Initially, the learning rate should be set high to make large adjustements to the weights. As
the training proceeds, the learning rate should decrease in order to fine-tune the network.
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