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KBS Development Stage 1: analysis of the problem that produces a representation of the problem that can be manipulated by the reasoning system - this representation is often a set of attribute values. Stage 2: developing the reasoning mechanism that manipulates the problem representation to produce a solution.
Aim to obtain desired outputs for each training example.
Backpropagation is the most popular learning algorithm.
Initialise all weights associated with inputs to each PE.
Present sample inputs to ANN.
Compare ANN outputs with desired output.
Alter weights to reduce the mean square error, and repeat.
until the error is within some tolerance.
Overfitting Training time Error In-sample error Generalisation error Too much training will result in a ( k -NN or ANN) model that makes minimal errors on the training data (memorises), but no longer generalises well. Beware.
ANN Development Collect data Separate into training and test sets Define a network structure Select a learning algorithm Set parameters, values, weights Transform data to network inputs Start training, revise weights Stop and test Use the network for new cases. Get more better data Reseparate Redefine structure Select another algorithm Reset Reset