01-25 2dal The data points of a binary classification problem with two features and the class...

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01-25 2dal The data points of a binary classification problem with two features and the class labels (+,-) are given below: 2023/10/ 46 202 -). (5. 1.-). (0.4. 10/0+). (20+). -). (-3.2. +). (0.- -4.-). (1. 5. +).(-1. +). (-2.0. +). 202377076 Derive the equation of the decision boundaries for positive and negative examples. Estimate the width of the decision boundary. [2+2+2=63] 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: 2022da040 K(UV)= (U.V+1)² [4+2-63] a) Construct the function function such that K(U. V)= q(U). 4(V) 1-2022da0 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. 20723 01-25 2dal The data points of a binary classification problem with two features and the class labels (+,-) are given below: 2023/10/ 46 202 -). (5. 1.-). (0.4. 10/0+). (20+). -). (-3.2. +). (0.- -4.-). (1. 5. +).(-1. +). (-2.0. +). 202377076 Derive the equation of the decision boundaries for positive and negative examples. Estimate the width of the decision boundary. [2+2+2=63] 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: 2022da040 K(UV)= (U.V+1)² [4+2-63] a) Construct the function function such that K(U. V)= q(U). 4(V) 1-2022da0 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. 20723

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