Abstract
In the two preceding chapters we discussed two closely related models, McCulloch—Pitts units and perceptrons, but the question of how to find the parameters adequate for a given task was left open. If two sets of points have to be separated linearly with a perceptron, adequate weights for the computing unit must be found. The operators that we used in the preceding chapter, for example for edge detection, used hand customized weights. Now we would like to find those parameters automatically. The perceptron learning algorithm deals with this problem.
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© 1996 Springer-Verlag Berlin Heidelberg
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Rojas, R. (1996). Perceptron Learning. In: Neural Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-61068-4_4
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DOI: https://doi.org/10.1007/978-3-642-61068-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60505-8
Online ISBN: 978-3-642-61068-4
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