If the Perceptron Learning Rule is applied to the current weights, using training item (a) and a learning rate of n=1.0, the new values for wo, W1 and w2 at the end of this training step will be:

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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Consider a Perceptron whose output is given by h(wo+w1×1+W2X2), where x1, X2 are inputs and h() is the Heaviside
(step) function.
Assume this Perceptron is being trained on the data in the following table, and that the current values of the weights
are wo= -0.5, W1= -1 and w2= -2.
Training Examplex1 x2 Class
|-1-1 Neg
2 1 Neg
(a)
(b)
(c)
|-2 2 Pos
If the Perceptron Learning Rule is applied to the current weights, using training item (a) and a learning rate of n=1.0,
the new values for wo, W1 and w2 at the end of this training step will be:
* W:
Transcribed Image Text:Consider a Perceptron whose output is given by h(wo+w1×1+W2X2), where x1, X2 are inputs and h() is the Heaviside (step) function. Assume this Perceptron is being trained on the data in the following table, and that the current values of the weights are wo= -0.5, W1= -1 and w2= -2. Training Examplex1 x2 Class |-1-1 Neg 2 1 Neg (a) (b) (c) |-2 2 Pos If the Perceptron Learning Rule is applied to the current weights, using training item (a) and a learning rate of n=1.0, the new values for wo, W1 and w2 at the end of this training step will be: * W:
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