a) The data points of a binary classification problem with two features and the class labels...
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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 negative examples. Estimate the width of the decision boundary. [2+2+2=6M] b) Consider a polynaial kernel (U, V) that transforms a twodimensional feature space U and V into higher order feature space using the function: [4+2 =6M] K(U.V) = (U. V + 1) a) Construct the function o function such that k(U, V) = 4(U). (V) 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. 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 negative examples. Estimate the width of the decision boundary. [2+2+2=6M] b) Consider a polynaial kernel (U, V) that transforms a twodimensional feature space U and V into higher order feature space using the function: [4+2 =6M] K(U.V) = (U. V + 1) a) Construct the function o function such that k(U, V) = 4(U). (V) 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.
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Introduction to Java Programming, Comprehensive Version
ISBN: 9780133761313
10th Edition
Authors: Y. Daniel Liang
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