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Suppose you are given the following multi-class classification training data, where each input example has three features and output label takes a value from good, bad, and ugly. • x1=(0, 1, 0) and y1=good • x2=(1, 0, 1) and y2=bad • 13=(1, 1, 1) and y3=ugly • x4=(1, 0, 0) and y4=bad • 15=(0, 0, 1) and y5=good

Suppose you are given the following multi-class classification training data, where each input example has three features and output label takes a value from good, bad, and ugly. • x1=(0, 1, 0) and y1=good • x2=(1, 0, 1) and y2=bad • 13=(1, 1, 1) and y3=ugly • x4=(1, 0, 0) and y4=bad • 15=(0, 0, 1) and y5=good

Computer Networking: A Top-Down Approach (7th Edition)
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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Suppose we want to learn a linear classifier using multi-class perceptron algorithm and start from the following weights: w_good=(0,0,0); w_bad=(0,0,0); and w_ugly=(0,0,0). Please do hand calculations to show how weights change after processing examples in the same order (i.e., one single pass over the five training examples).

Suppose you are given the following multi-class classification training data, where each input example has three features and output label takes a value from good, bad, and ugly. • x1=(0, 1, 0) and y1=good • 22=(1, 0, 1) and y2=bad • 13=(1, 1, 1) and y3=ugly • x4=(1, 0, 0) and y4=bad • 15=(0, 0, 1) and y5=good
Transcribed Image Text:Suppose you are given the following multi-class classification training data, where each input example has three features and output label takes a value from good, bad, and ugly. • x1=(0, 1, 0) and y1=good • 22=(1, 0, 1) and y2=bad • 13=(1, 1, 1) and y3=ugly • x4=(1, 0, 0) and y4=bad • 15=(0, 0, 1) and y5=good
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