AdaBoost

AdaBoost, Weak classifiers: GDA, Knn, Naive Bayes, Linear, SVM

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-