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- W2392371707 abstract "It is shown that multilayer networks can be used to approximate almost any function,if neurons are enough.In neural networks,people want to use the simplest network that can adequately represent the training set.Here is a common rule: Do not use a bigger network when a smaller network will work.However,no definite approach can be used to determinate the number of hidden layer neurons.In general,people determine the number through carrying out experiments many times.This paper presented a simple method for determining the number of hidden layer neurons in a neural network.This method integrated two techniques: decision tree based on maximum information gain of entropy and neural network.The depth of decision tree was used to determine the number of hidden layer neurons.This approach for determining the number of hidden layer neurons can produce a minimum scale network with good recognition rate.Two different experiments were done to show its validity." @default.
- W2392371707 created "2016-06-24" @default.
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- W2392371707 date "2010-01-01" @default.
- W2392371707 modified "2023-09-23" @default.
- W2392371707 title "Determination of minimal artificial neural network based on decision tree algorithm" @default.
- W2392371707 hasPublicationYear "2010" @default.
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