number of clusters to be formed is 2. The learning rate (or, gain term n(t)=0.8. Do the training for 5 epochs and compute the final weights. n(t+1)= n(t)/2 Vectors: 21000 00031 11000 0 0 0 0 2 Initial Weight Matrix is : 0.7 0.3 0.9 0.1 0.2 0.2 0.1 0.7 0.3 0.8 (Use the direction of the arrow in each epoch and divide the learning rate by 2 after each epoch. Do not forget to normalize the vectors
number of clusters to be formed is 2. The learning rate (or, gain term n(t)=0.8. Do the training for 5 epochs and compute the final weights. n(t+1)= n(t)/2 Vectors: 21000 00031 11000 0 0 0 0 2 Initial Weight Matrix is : 0.7 0.3 0.9 0.1 0.2 0.2 0.1 0.7 0.3 0.8 (Use the direction of the arrow in each epoch and divide the learning rate by 2 after each epoch. Do not forget to normalize the vectors
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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please help me step by step thank you
![USE KOHONEN SOM to cluster 4 vectors. The maximum
number of clusters to be formed is 2. The learning rate
(or, gain term n(t)=0.8. Do the training for 5 epochs and
compute the final weights.
n(t+1)= n(t)/2
Vectors:
| 2 100 0
0 0 0 31
1 1000
0 0 0 0 2
Initial Weight Matrix is :
0.7 0.3
0.9 0.1
0.2 0.2
0.1 0.7
0.3 0.8
(Use the direction of the arrow in each epoch and divide
the learning rate by 2 after each epoch . Do not forget to
normalize the vectors.)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd54b95dd-2a53-42a2-b68c-090534f57a7c%2Ff6dac6aa-8143-4d7d-b141-33b85cdacb72%2Fta707e4_processed.png&w=3840&q=75)
Transcribed Image Text:USE KOHONEN SOM to cluster 4 vectors. The maximum
number of clusters to be formed is 2. The learning rate
(or, gain term n(t)=0.8. Do the training for 5 epochs and
compute the final weights.
n(t+1)= n(t)/2
Vectors:
| 2 100 0
0 0 0 31
1 1000
0 0 0 0 2
Initial Weight Matrix is :
0.7 0.3
0.9 0.1
0.2 0.2
0.1 0.7
0.3 0.8
(Use the direction of the arrow in each epoch and divide
the learning rate by 2 after each epoch . Do not forget to
normalize the vectors.)
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