Show the decision boundaries produced by the

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Unformatted text preview: n tree algorithm on the same graph. Describe in one sentence (or function) how the decision boundaries translate to walls on the original, xy-plane graph. 6 Question 2: Neural Networks and Genetic Algorithms (50 points) Part A (20 points) Perceptrons are the basic units of neural networks as we have seen them. They take a list of inputs x, multiply them by a list of corresponding weights w, compute the sum of those products, and pass the result through a decision function. We use a "fake" input T, usually -1, times an associated weight wT, as part of the sum. When using a single perceptron for classification, one usually uses a threshold decision function: if the sum z > 0, the output is 1, otherwise 0. x1 x2 x3 ... xn w1 w2 w3 -1 wT ∑ wn To explore what perceptrons can and cannot do, we will ask you to make up weights, rather than training them. Consider the boolean function A→B, and note that it is the same as ¬A∨B. 1. Can a single perceptron with inputs A and B output 1 iff A→B? If yes give weights: wA=_______ wB=______...
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