Using the Perceptron learning rule, Find the weights required to perform the following classifications: 1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and therefore have target value 1. 2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and have target value 0.
Using the Perceptron learning rule, Find the weights required to perform the following classifications: 1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and therefore have target value 1. 2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and have target value 0.
Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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
Transcribed Image Text:Using the Perceptron learning rule, Find the weights required to perform
the following classifications:
1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and
therefore have target value 1.
2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and
have target value 0.
Use a learning rate of 1.1, 0 =0.35, and starting weights of 0. Using each
of the training vectors as input, test the responses of the net.
Please submit using model
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