Programming 4 Bayes Classifier

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  • Goal : Practice the Bayes classifier examples, and understand its relationship with minimum distance classifier.
  • Instructions :
    • Read Section 1.4 of the AMA book (S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Introduction to Pattern Recognition: A Matlab Approach, Academic Press, 2010 [PDF download from FJU Library] )
    • Practice Example 1.4.1, 1.4.2, 1.4.3.
    • Complete Exercise 1.4.1, 1.4.2, 1.4.3, 1.4.4.
    • (Bonus) Rewrite the Example 1.4.3 code by replacing the two random data sets X (training set) and X1(test set) with fisher iris data set. You need to randomly select 40 samples of each class for X, and the remaining 10 samples of each class for X1. Then you have to calculate the mean and covariance matrix of each class by X, which represent the m1, m2, m3, S1, S2, S3 in Example 1.4.3.
    • Write a report with a web page.
  • Readings : AMA Sec. 1.4
    • (AMA) Chapter 1 Classifiers based on Bayes Decision Theory [PDF]
        • Section 1.4 Minimum Distance Classifier
  • Report : Create a web page with text descriptions (Chinese or English) and images/pictures to
      • Explain Bayes classifier and minimum distance classifier.
      • Describe the purpose of examples and exercises
      • Explain the program codes of examples and exercises
      • Explain the results of examples and exercises
  • Submit your web address by Google Classroom.