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##### CS4378V_CS5369L: Homework #2;CS4378V_CS5369L: Homework #2;Assigned: Tuesday, October 9, 2012;Due: Tuesday, October 23, 2012;(100 points)CS4378V_CS5369L: Homework #2;1. (40 pts) Implement...

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CS4378V_CS5369L: Homework #2;CS4378V_CS5369L: Homework #2;Assigned: Tuesday, October 9, 2012;Due: Tuesday, October 23, 2012;(100 points)CS4378V_CS5369L: Homework #2;1. (40 pts) Implement the linear regression algorithm (gradient descent) and run the linear;regression algorithm on the training samples (linear_regression_data.txt). You can find;the dataset on Tracs under ?Resources: Data?. Get the learned linear function and the;predicted results of the testing samples (linear_regression_data.txt).;2. (40 pts.) Learn the probabilities for Naive Bayes for the following training examples. Use;Laplace?s smoothing to estimate conditional probabilities.;How well does your Naive Bayes hypothesis perform on the following test examples?;Show your work.;Attributes Class;x1 x2 x3 x4 y;0 0 1 0 1;0 1 0 1 1;1 0 0 0 -1;1 0 1 1 -1;3. (20 pts) Design an n-input perceptron that implements the function: if k or more of the;inputs are true, the output is true. (Suppose the input value can only be ?1? (true) or ?0?;(false))

Paper#73339 | Written in 18-Jul-2015

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