Unformatted text preview: Biological Neural Computation
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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