23/10 a) The data points of a binary classification problem with two features and the class...

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23/10 a) The data points of a binary classification problem with two features and the class labels are given below: (0, 2, 222 +240 Derive the equation of the decision boundaries for positive and negative examples. Estimate the width of the decision boundary. [2+2+2=6M] 023/ 0/01-2922da 202404664 (1,0,-), (-3, 2, +), (0, -4,-), (1, 5, +), (-1.-6. -). (5.1, -), (0.4, +), (-2, 0, +), 046 b) Consider a polynomial kernel K(U, V) that transforms a two-dimensional feature space U and V into higher order feature space using the function: [4+2=6M] K(U.V) = (U.V+ 1)² 2da0 a) Construct the function o function such that K(U, V) = 4(U). ❤(V) b) Given the feature vectors XI(1, 1), and X2 (0, 1), compute their corresponding feature vectors in the new feature space by applying the function (X) obtained above. 23/10 a) The data points of a binary classification problem with two features and the class labels are given below: (0, 2, 222 +240 Derive the equation of the decision boundaries for positive and negative examples. Estimate the width of the decision boundary. [2+2+2=6M] 023/ 0/01-2922da 202404664 (1,0,-), (-3, 2, +), (0, -4,-), (1, 5, +), (-1.-6. -). (5.1, -), (0.4, +), (-2, 0, +), 046 b) Consider a polynomial kernel K(U, V) that transforms a two-dimensional feature space U and V into higher order feature space using the function: [4+2=6M] K(U.V) = (U.V+ 1)² 2da0 a) Construct the function o function such that K(U, V) = 4(U). ❤(V) b) Given the feature vectors XI(1, 1), and X2 (0, 1), compute their corresponding feature vectors in the new feature space by applying the function (X) obtained above.

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a Deriving the Equation of Decision Boundaries for Positive and Negative Examples Given the data poi... View the full answer