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

By Theodoridis S., et al.

An accompanying guide to Theodoridis/Koutroumbas, development popularity, that comes with Matlab code of the commonest tools and algorithms within the publication, 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 most typical tools and algorithms in Theodoridis/Koutroumbas, trend reputation 4e.*Solved examples in Matlab, together with real-life facts units in imaging and audio recognition*Available individually or at a different package deal cost with the most textual content (ISBN for package deal: 978-0-12-374491-3)

Show description

Read Online or Download An introduction to pattern recognition: A MATLAB approach PDF

Similar software: systems: scientific computing books

Transforms and Applications Primer for Engineers with Examples and MATLAB

Transforms and purposes Primer for Engineers with Examples and MATLAB® is needed studying for engineering and technological know-how scholars, pros, and somebody engaged on difficulties concerning transforms. This priceless primer includes the main crucial crucial transforms that either working towards engineers and scholars have to comprehend.

Biosignal and Medical Image Processing: MATLAB-Based Applications (Signal Processing and Communications)

Depending seriously on MATLAB® difficulties and examples, in addition to simulated information, this text/reference surveys an enormous array of sign and photograph processing instruments for biomedical functions, offering a operating wisdom of the applied sciences addressed whereas showcasing precious implementation methods, universal pitfalls, and crucial software options.

Modeling of Curves and Surfaces with MATLAB®

This article on geometry is dedicated to varied important geometrical themes together with: graphs of services, alterations, (non-)Euclidean geometries, curves and surfaces in addition to their purposes in various disciplines. This e-book offers user-friendly equipment for analytical modeling and demonstrates the opportunity of symbolic computational instruments to help the advance of analytical strategies.

Intelligent Routines: Solving Mathematical Analysis with Matlab, Mathcad, Mathematica and Maple

Genuine research is a self-discipline of in depth examine in lots of associations of upper schooling, since it comprises worthy strategies and basic leads to the learn of arithmetic and physics, of the technical disciplines and geometry. This ebook is the 1st one in every of its sort that solves mathematical research issues of all 4 comparable major software program Matlab, Mathcad, Mathematica and Maple.

Additional info for An introduction to pattern recognition: A MATLAB approach

Example text

14) i=1 From the previous relation, it becomes clear that the explicit form of the mapping function φ(·) is not required; all we have to know is the kernel function since data appear only in inner products. Observe that the resulting discriminant function, g(x), is nonlinear because of the nonlinearity of the kernel function. 4, may be used. The input argument kernel takes the values poly for the polynomial kernel or rbf for the RBF kernel. In the former case, kpar1 and kpar2 correspond to the β and n parameters, respectively; in the latter case, kpar1 corresponds to the σ parameter.

Note that this is very low, indicating a good estimate. Step 3. 4 Support Vector Machines: The Linear Case 43 Alternatively, use the bayes_classifier MATLAB function. 82% (estimated based on X2 ). Some comments are in order. First, it is easy to check that the probability estimates may be greater than 1 or less than 0. , nonnegativity and sum to 1). However, the sum of the estimates of the a posteriori probabilities for a given vector is very close to 1. 1 1. 3 where now the means of the three classes ω1 , ω2 , and ω3 are m1 = [0, 0, 0]T , m2 = [1, 2, 2]T , and m3 = [3, 3, 4]T , respectively.

Repeat the previous steps, first with X2 replaced by a set X3 containing N3 = 10,000 data vectors and then with a set X4 containing N4 = 100,000 data vectors. Both X3 and X4 are generated using the prescription adopted for X1 . Comment on the results. Solution. Do the following: Step 1. To ensure reproducibility of the results, set seed = 0 for the randn MATLAB function for the generation of X1 ; for the generation of X2 , X3 , and X4 set seed = 100. 75]; P=[1/2 1/2]; To generate X1 and the required class labels (1 for ω1 , 2 for ω2 ), type N1=200; randn('seed',0) X1=[mvnrnd(m(:,1),S,fix(N1/2)); mvnrnd(m(:,2),S,N1-fix(N1/2))]'; z1=[ones(1,fix(N1/2)) 2*ones(1,N1-fix(N1/2))]; X2 is generated in a similar fashion.

Download PDF sample

Rated 4.46 of 5 – based on 42 votes