2022dal a) The data points of a binary classification problem with two features and the class...

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2022dal a) The data points of a binary classification problem with two features and the class labels (+ -) are given below: (0, 2, +), (1.0,-). (-3, 2, +). (0, -4,-). (1.5, +).(-1.6, -). (5. 1. -). (0. 4. +), (-2.0, +). Derive the equation of the decision boundaries for positive and neg Estimate the width of the decision boundary. 8-8554 b) Consider a polynomial kernel x(U. V) that transforms a two-dimensional feature space U and V into higher order feature space using the function: and v0/01. K(U.V) = (U.V + 1)² and negative examples. [2+2+2=6M] a) Construct the function o function such that x(U. V) = o(U). 4(V) 28-8552- b) Given the feature vectors X1(1, 1), and X2 (0, 1), compute their corresponding feature vectors in the new feature space by applying the function (X) obtained above. 1(1,1) 438/855-40-20 the new Options 7922dau 28-85540-2023/10/ 2022dal a) The data points of a binary classification problem with two features and the class labels (+ -) are given below: (0, 2, +), (1.0,-). (-3, 2, +). (0, -4,-). (1.5, +).(-1.6, -). (5. 1. -). (0. 4. +), (-2.0, +). Derive the equation of the decision boundaries for positive and neg Estimate the width of the decision boundary. 8-8554 b) Consider a polynomial kernel x(U. V) that transforms a two-dimensional feature space U and V into higher order feature space using the function: and v0/01. K(U.V) = (U.V + 1)² and negative examples. [2+2+2=6M] a) Construct the function o function such that x(U. V) = o(U). 4(V) 28-8552- b) Given the feature vectors X1(1, 1), and X2 (0, 1), compute their corresponding feature vectors in the new feature space by applying the function (X) obtained above. 1(1,1) 438/855-40-20 the new Options 7922dau 28-85540-2023/10/

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a To derive the equation of the decision boundaries for positive and negative examples in a binary classification problem we typically use linear clas... View the full answer