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AdaBoost

version 1.0.0.0 (227 KB) by Bhartendu
AdaBoost, Weak classifiers: GDA, Knn, Naive Bayes, Linear, SVM

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Updated 28 May 2017

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AdaBoost Demo, with various Weak classifiers:
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AdaBoost :
AdaBoost (Adaptive Boosting) generates a sequence of hypothesis and combines them with weights.

::Choosen Weak classifiers::
1. GDA
2. Knn (NumNeighbors = 30)
3. Naive Bayes
4. Linear (Logistic Regression*)
5. SVM ('KernelFunction: rbf')

Refer to: https://www.iist.ac.in/sites/default/files/people/in12167/adaboost.pdf

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Contents:
1. Initialization (Dataset:: NoisyData.csv)
2. Gaussian Discriminant Analysis Classification
3. Knn Classification
4. Naive Bayes Classification
5. Logistic Regression
6. SVM (rbf) Classification
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| Adaboost (GDA, Knn, NB, Logistic, SVM) |
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7. Conclusions

Related Examples:
1. SVM
https://in.mathworks.com/matlabcentral/fileexchange/63158-support-vector-machine

2. SVM using various kernels
https://in.mathworks.com/matlabcentral/fileexchange/63033-svm-using-various-kernels

3. SVM for nonlinear classification
https://in.mathworks.com/matlabcentral/fileexchange/63024-svm-for-nonlinear-classification

4. SMO
https://in.mathworks.com/matlabcentral/fileexchange/63100-smo--sequential-minimal-optimization-

5. AdaBoost+ PCA
https://in.mathworks.com/matlabcentral/fileexchange/63161-adaboost--pca--capstone-project-

Cite As

Bhartendu (2020). AdaBoost (https://www.mathworks.com/matlabcentral/fileexchange/63162-adaboost), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (7)

Faulty Program

Zhimin

for i=1:length(D)
p = (p_max-p_min)*rand(1) + p_min;

if D(i)>=p
d(i,:)=a(i,:);
end
t=randi(size(d,1));
Dt=[Dt ;d(t,:)];
end
Hello,What is the role of this part?the weight of a single sample does not seem to affect the sample selection of later training sets,The training samples of each weak classifier seem to be randomly selected. Where is the relationship between the training samples and the weights of the previous weak classifier?
need your answer. Thank you

Hello is it possible to use MLP as weak classifier?

Aruna N

Error using horzcat
Dimensions of matrices being concatenated are not consistent.

Error in adaboost (line 17)
a=[Xtrain Ytrain];

Error in MAIN (line 180)
[result,u]=adaboost( Trainfea, label,featq);

ahmed usama

thank you for that
need the reference paper
ahmed

No One

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
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