Combining Pattern Classifiers(Using Matlab), 2nd Edition

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Combining Pattern Classifiers 2nd Edition
Methods and Algorithms

Book Description A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Book Details Publisher: Wiley By: Ludmila I. Kuncheva ISBN: 978-1-118-31523-1 Year: 2014 Pages: 384 Language: English File size: 8.9 MB File format: PDF 本帖隐藏的内容 Combining Pattern Classifiers, 2nd Edition.pdf (8.51 MB, 需要: 25 个论坛币) 2015-3-28 01:36:51 上传 需要: 25 个论坛币  [购买]

• http://pages.bangor.ac.uk/~mas00a/
• http://ca.wiley.com/WileyCDA/WileyTitle/productCd-1118315235.html
  

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关键词：classifiers classifier combining Edition Pattern discipline published coherent current methods

Description

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition

The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition ofCombining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods.

Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes:

• Coverage of Bayes decision theory and experimental comparison of classifiers

• Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others

• Chapters on classifier selection, diversity, and ensemble feature selection

With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.

http://ca.wiley.com/WileyCDA/WileyTitle/productCd-1118315235.html

1. %---------------------------------------------------------%
   
2. C = naive_bayes_train([x y], labels);
   
3. la = naive_bayes_classify(C,[x y]);
   
4. %---------------------------------------------------------%
   
5. %---------------------------------------------------------%
   
6. function C = naive_bayes_train(data, labels)
   
7. % --- train parametric NB classifier
   
8. for i = 1:max(labels)
   
9. c_index = labels == i;
   
10. C(i).prior = mean(c_index);
    
11. C(i).mean = mean(data(c_index,:),1);
    
12. if sum(c_index) > 1 % class with 1 object
    
13. C(i).std = std(data(c_index,:));
    
14. else
    
15. C(i).std = zeros(1,size(data,2));
    
16. end
    
17. end
    
18. %---------------------------------------------------------%
    
19. %---------------------------------------------------------%
    
20. function labels = naive_bayes_classify(C, data)
    
21. % --- classify with the trained NB classifier
    
22. g = zeros(numel(C),size(data,1));
    
23. for i = 1:numel(C)
    
24. Ms = repmat(C(i).mean,size(data,1),1);
    
25. Ss = repmat(C(i).std,size(data,1),1);
    
26. t = 1./Ss .* exp(-(data - Ms).ˆ2 ./(2* Ss.ˆ2));
    
27. g(i,:) = prod(t') * C(i).prior;
    
28. end
    
29. [˜ ,labels] = max(g); labels = labels(:);
    
30. %---------------------------------------------------------%

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