An introduction to pattern recognition: A MATLAB approach by Theodoridis S., et al.

By Theodoridis S., et al.

An accompanying handbook to Theodoridis/Koutroumbas, development acceptance, that comes with Matlab code of the most typical tools and algorithms within the ebook, including a descriptive precis and solved examples, and together with real-life facts units in imaging and audio acceptance. *Matlab code and descriptive precis of the commonest equipment and algorithms in Theodoridis/Koutroumbas, development popularity 4e.*Solved examples in Matlab, together with real-life information units in imaging and audio recognition*Available individually or at a distinct package deal cost with the most textual content (ISBN for package deal: 978-0-12-374491-3)

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Step 5. 2, 20, 200. 2 1. Generate a 2-dimensional data set X1 (training set) as follows. Consider the nine squares [i, i + 1] × [ j, j + 1], i = 0, 1, 2, j = 0, 1, 2 and draw randomly from each one 30 uniformly distributed points. The points that stem from squares for which i + j is even (odd) are assigned to class +1 (−1) (reminiscent of the white and black squares on a chessboard). 1, set the seed for rand at 0 for X1 and 100 for X2 ). 2. 001. Compute the training and test errors and count the number of support vectors.

8]; S(:,:,1)=S;S(:,:,2)=S; P=[1/2 1/2]'; N_1=1000; randn('seed',0) [X1,y1]=generate_gauss_classes(m,S,P,N_1); N_2=5000; randn('seed',100) [X2,y2]=generate_gauss_classes(m,S,P,N_2); Step 2. 12%. Note that different seeds for the randn function are likely to lead to slightly different results. 1 for k = 1,7,15. For each case compute the classification error rate. Compare the results with the error rate obtained by the optimal Bayesian classifier, using the true values of the mean and the covariance matrix.

Use the Rotate 3D button to view the data set from different angles. Next, define the c × N1 dimensional matrix z1 , each column of which corresponds to a training point. Specifically, its ith column elements equal zero except one, which equals unity. The position of the latter indicates the class where the corresponding vector xi of X1 belongs. z1=zeros(c,N1); for i=1:N1 z1(y1(i),i)=1; end 42 CHAPTER 2 Classifiers Based on Cost Function Optimization In a similar manner, generate X2 and z2 .

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