2. The following table contains seven observations in two dimensions, X1 and X2. Each observation has an associated clas

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2 The Following Table Contains Seven Observations In Two Dimensions X1 And X2 Each Observation Has An Associated Clas 1 (84.79 KiB) Viewed 74 times

2. The following table contains seven observations in two dimensions, X1 and X2. Each observation has an associated class label Y: Green and Yellow. The observations are plotted in a 2-Dimensional space. What is the equation for the maximal margin separating hyperplane? x X2 Y 3 4 Green 2 2 Green 4 4 Green 1 4 Green 2 1 Yellow 4 3 Yellow 4 1 Yellow (A table of observations with two dimensions(X1,X2) and their corresponding label(Y). The observations and their corresponding labels are (x1=3, X2=4, Y=Green), (X1=2, X2=2, Y=Green, JX1=4, X2=4, Y=Green), (X1=1, X2=4, Y=Green), (x1=2, X2=1, Y=Yellow), (X1=4, X2=3, Y=Yellow), (X1=4, X2=1, Y=Yellow) 5 4 3 2 1 0 X1 (A plot of 7 points belonging to two classes, Yellow and green. The labels of X-axis and Y-axis are X1, X2 respectively. The points associated with Green labels are at (x1=3, X2=4), (X1=2, X2=2), (X1=4, X2=4, XX1=1, X2=4). The points associated with Yellow class are (X1=2, X2=1), (X1=4, X2=3), (X1=4, X2=1)) 42 – X1 + X2 = 0 O 2 - X1 + X2 = 0 O 42+ X1 + X2 = 0 O 42+ X1 - X2 = 0

3. Which of the following data sets do NOT require a kernel transformation to transform data into higher dimensions where it can be separated with a hyperplane? A A Figure 1 Figure 2 A AAA AAA A A Figure 3 (Three figures, Figure1 has points of two classes completely mixed and there is no separation. Figure2, half points of each class are mixed and the other half are separated. Figure 3, the two classes are clearly separated.) Figure 1 Figure 2 Figure 3 4. Which of the following is a point on the hyperplane 1 + 2X1 + 2X2 = 0? (-1/4-1/4) Ο Ο (-1,1) (0,-1) (-1/2-1/2)

5. Which of the following is NOT true for SVMS? SVMs try to lift the problem into a space where the data is linearly separable. For linearly separable data of two classes, SVM can find multiple hyperplanes that can correctly classify all samples with different margins. Only support vectors play a role in the classifier obtained for SVMs.