Artificial Intelligence (CS607)
© Copyright Virtual University of Pakistan
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4.3 Supervised
Given a set of example input/output pairs, find a rule that does a good job of
predicting the output associated with a new input.
4.3.1 Back propagation algorithm
1. Randomize the weights {ws} to small random values (both positive and
negative)
2. Select a training instance t, i.e.,
a. the vector {xk(t)}, i = 1,...,Ninp (a pair of input and output patterns),
from the training set
3. Apply the network input vector to network input
4. Calculate the network output vector {zk(t)}, k = 1,...,Nout
5. Calculate the errors for each of the outputs k , k=1,...,Nout, the difference
between the desired output and the network output
6. Calculate the necessary updates for weights -ws in a way that minimizes
this error
7. Adjust the weights of the network by - ws
8. Repeat steps for each instance (pair of input–output vectors) in the
training set until the error for the entire system
4.4 Unsupervised
• Given a set of examples with no labeling, group them into sets called
clusters
• A cluster represents some specific underlying patterns in the data
• Useful for finding patterns in large data sets
• Form clusters of input data
• Map the clusters into outputs
• Given a new example, find its cluster, and generate the associated output
4.4.1 Self-organizing neural networks: clustering, quantization,
function approximation, Kohonen maps
1. Each node's weights are initialized
2. A data input from training data (vector) is chosen at random and
presented to the cluster lattice
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