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Chapter 8.2 pt 4

CGSC170 Chapter 8.2 pt 4:

Cognitive Science
Course Code
Kaja Jasinka
8.2 pt 4

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Frank Rosenblatt
Studied learning in single layer networks
Looking for a learning rule that would permit a network with
random weights and a random threshold to settle on a
configuration of weights and thresholds that would allow it
to solve a given problem
Solving it would mean that the correct output for
every input is produced
Supervised learning
If it was wrong, there was a weight
or threshold issue
Learning in neural networks
means that the weights alter
as a response to error
Successful when these
changes in weights/threshold
converge upon a
configuration that always
makes the desired output for
an input
Perceptron Convergence Rule
Similar to Hebbian Learning
Relies on basic principles that changes in
weight are determined only by what
happens locally
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