ln022 - Neural Network Learning Here we look at the...

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Neural Network Learning Here we look at the practical considerations for constructing artificial neural networks: (1) All training data has to be numerical , even categorical data has to be mapped into numerical values (sub-symbolic learner) (2) It is customary to normalize the data into the interval [0,1], or something close to it. (3) We need to pick a topology for the network Do we need a hidden layer? If so, how many nodes in the hidden layer? (4) We need to pick a learning rate . (5) We need to pick a convergence criterion that will tell us whether we learned the concept/training examples successfully.
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Tiberius Data Mining Suite Create a new neural network.
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Tiberius Data Mining Suite Select a CSV file for training. NOTE: ignore the tip dialog box.
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Tiberius Data Mining Suite Select the neural network inputs and output; the inputs are given by your independent attributes, the output is your target attribute. Hit OK when you are done.
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Tiberius Data Mining Suite Learning Rate Training Iterations Training Status Error Start Training Training Window Check Results Window Generate Code Window
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