Consider the classification problem defined below. {P = []t=1} {p = [1],t = 1) {p3 =...

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Consider the classification problem defined below. {P₁ = [₁¹]₁t₁=1} {p₂ = [1],t₂ = 1) {p3 = [],t3 = 0} {P₁ = [], t₁ = 0} tz i. Design a single-neuron perceptron to solve this problem. Design the network graphically, by choosing weight vectors that are orthogonal to the decision boundaries. ii. Test your solution with all four input vectors. iii. Classify the following input vectors with your solution. You can either perform the calculations manually or with MATLAB. P6 = [] P8 = [2] 2. (E4.4) Solve the classification problem in E4.2 by applying the perceptron rule to the following initial parameters, and repeat parts (ii) and (iii) with the new solution. W(0) = [0 0] b(0) = 0 ps = [²] Ps P7 =[9] Consider the classification problem defined below. {P₁ = [₁¹]₁t₁=1} {p₂ = [1],t₂ = 1) {p3 = [],t3 = 0} {P₁ = [], t₁ = 0} tz i. Design a single-neuron perceptron to solve this problem. Design the network graphically, by choosing weight vectors that are orthogonal to the decision boundaries. ii. Test your solution with all four input vectors. iii. Classify the following input vectors with your solution. You can either perform the calculations manually or with MATLAB. P6 = [] P8 = [2] 2. (E4.4) Solve the classification problem in E4.2 by applying the perceptron rule to the following initial parameters, and repeat parts (ii) and (iii) with the new solution. W(0) = [0 0] b(0) = 0 ps = [²] Ps P7 =[9]

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Answer E42 a we need to classify red and blue as 1 ... View the full answer