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Get An introduction to pattern recognition: A MATLAB approach PDF

By Theodoridis S., et al.

ISBN-10: 0123744865

ISBN-13: 9780123744869

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

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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.

SMO2(X , y , kernel, kpar1, kpar2, C, tol, steps, eps, method) 46 CHAPTER 2 Classifiers Based on Cost Function Optimization where its inputs are a matrix X containing the points of the data set (each row is a point), the class labels of the data points ( y ), the type of kernel function to be used (in our case linear ), two kernel parameters kpar1 and kpar2 (in the linear case both are set to 0), the parameter C, the parameter tol, the maximum number of iteration steps of the algorithm, a threshold eps (a very small number, typically on the order of 10−10 ) used in the comparison of two numbers (if their difference is less than this threshold, they are considered equal to each other), the optimization method to be used (0 →Platt, 1 →Keerthi modification 1, 2 →Keerthi modification 2),1 alpha is a vector containing the Lagrange multipliers corresponding to the training points, w0 is the threshold value, w is the vector containing the hyperplane parameters, returned by the algorithm.

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.

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


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