Program1 - The function must display the MSE on the...

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Input is a column vector containing N desired output values selected from {-1,1}, is a learning rate value, (optional arguments, i.e., either neither are provided or both are provided) if provided, specify the range of x and y values over which to display the class confidence values ( v values) Given that the training set is a linearly separable collection of training vectors, the function should use the perceptron learning rule to identify weights that will allow a perceptron to correctly classify all elements of the training set. If you want, you can make your learning rate change as a function of time.
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Unformatted text preview: The function must display the MSE on the training set at the end of each training epoch. The MSE should be calculated as follows: . Compute this over the set X of misclassified training vectors. (Note: the divisor N is the total number of training vectors.) where output is a 2 N matrix containing samples from the moons, generated by uniformly sampling an angle (in range 0, 2 ) and radius (in range r w/2), output is an N element column vector containing value 1 if the point is in the top moon and value -1 if the point is in the bottom moon....
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This note was uploaded on 11/30/2011 for the course CAP 6615 taught by Professor Staff during the Spring '08 term at University of Florida.

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Program1 - The function must display the MSE on the...

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