By Theodoridis S., et al.
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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;MATLAB programming for enginears КНИГИ ;НАУКА и УЧЕБА Название: MATLAB programming for enginears Автор: Chapman S. J. Год: 2002 Издательство: CL-Engineering Страниц: 567 Формат: djvu Размер: seventy two Mb Язык: английскийАннотация. Emphasizing problem-solving abilities all through this very profitable ebook, Stephen Chapman introduces the MATLAB® language and exhibits the way to use it to resolve standard technical difficulties.
The aim of this consultant is to provide a brief advent on how you can use Maple. It essentially covers Maple 12, even supposing many of the advisor will paintings with past types of Maple. additionally, all through this consultant, we'll be suggesting suggestions and diagnosing universal difficulties that clients are inclined to come upon. this could make the educational strategy smoother.
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Dieses Buch ist als Einf? hrung in MATHCAD f? r Anf? nger, als ? bungsbuch neben Mathematikvorlesungen und als umfassendes Handbuch zum Nachschlagen geeignet. Angesprochen werden Studenten an Hochschulen, Fachhochschulen und Berufsakademien sowie Sch? ler der gymnasialen Oberstufe. Der Autor erschlie?
Extra resources for An introduction to pattern recognition: A MATLAB approach
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.
An introduction to pattern recognition: A MATLAB approach by Theodoridis S., et al.