HW2.pdf - Biological Neural Computation Homework problem set2 Spring 2013 General Guidelines The homework solutions should include figures that clearly

HW2.pdf - Biological Neural Computation Homework problem...

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Unformatted text preview: Biological Neural Computation Homework problem set2 Spring 2013 General Guidelines: The homework solutions should include figures that clearly capture the result. The figures have to be labeled, well explained and the results must be clearly discussed. The first sheet of the homework must certify that this is completely your work and list the students/people you have consulted or received help from (with your signature and date of submission). All online references used must be listed in the reference section at the end of the homework. Good luck, Barani Raman Problem 1. Implement the batch perceptron algorithm to obtain a linear discriminating function as described in Chapter 5 of Duda et al Pattern Classification book. Create linearly separable and non-­‐linearly separable datasets with samples belonging to the two classes. Apply your perceptron algorithm to discriminate. Report you observation and analysis? Plot classification error vs # of iterations, classification results, and the obtained decision boundary. [30 pts] Problem 2: Using the same datasets used in problem 1, now create a linear classifier using Least Mean Squares (LMS) rule. Compare these results with the Perceptron algorithm results. [30 pts] Problem 3. (1) Generate 100 random (x, y) points, and run a 2D-­‐lattice SOM with 100 neurons in a 10by10 lattice. Show that the SOM can perform density estimation by generating random points that belong to various distributions (uniform, Gaussian etc.). [40 pts] ...
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  • Spring '14
  • Raman

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