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Unformatted text preview: CS446: Pattern Recognition and Machine Learning Fall 2009 Problem Set 3 Handed Out: September 24, 2009 Due: October 8, 2009 Feel free to talk to your classmates about the homework. I am more concerned that you learn how to solve the problem than that you demonstrate that you solved it entirely on your own. You should, however, write down your solution yourself. Please try to keep the solution brief and clear. Please, no handwritten solutions. Be sure your name appears on the top of each page. Please present your algorithms in both pseudocode and English. That is, give a precise formulation of your algorithm as pseudocode and also explain in one or two concise paragraphs what your algorithm does. Be aware that pseudocode is much simpler and more abstract than real code. Take a look at the textbook pseudocode (e.g. Table 2.5 on page 33) to get an idea about the appropriate level of abstraction. The homework is due at 4:00 pm on the due date. Email writeup and your code to the TA. Please do NOT hand in a hard copy of your writeup. Please put < userid > CS446 hw3 submission as the subject line of the email when you submit your homework to mchang21@uiuc.edu . Put all of your files into a single compressed file with the file name < userid >hw3.tgz. 1. [Online Algorithm Comparison  100 points] In this problem set you will implement several online learning algorithms including Winnow and Perceptron , and experiment with them by comparing their performance on a synthetic dataset. For this problem set, we recommend using Matlab, given that we supply Matlab code that can help you run the experiments. In these experiments you will generate examples that are labeled according to a simple threshold function , an lof mof n function. That is, the function is defined on the ndimensional Boolean cube { , 1 } n , and there is a set of m attributes such that an example is positive iff at least l of these m are active in the example. We call l , m and n concept parameters , since they specify what exact concept we are talking about. To make sure you understand the function, try to write it for yourself as a linear threshold function. This way, you will make sure that Winnow and Perceptron can represent this function. Also, notice that this concept is a generalization of mono tone conjunctions and of monotone disjunctions, which you get for l = m and l = 1 respectively. Your algorithm does not know the target function and does not know which are the relevant attributes or how many are relevant. The goal is to evaluate these algorithms under several conditions and derive some conclusions on their relative advantages and disadvantages. The instance space is { , 1 } n (that is, there are n boolean features in the domain). You will run experiments on several values of l , m , and n ....
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This note was uploaded on 04/23/2010 for the course COMPUTER S cs 446 taught by Professor Ahuja during the Fall '08 term at University of Illinois at Urbana–Champaign.
 Fall '08
 ahuja
 Machine Learning

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