MATLAB Implementation of AdaBoost Algorithm with Code Explanations

This MATLAB program implements the AdaBoost algorithm and represents my initial exploration into AdaBoost research. The code demonstrates practical value by providing a working implementation that helps deepen understanding of AdaBoost's fundamental principles and applications. Through this program, I learned how to implement the AdaBoost algorithm in MATLAB, including key components such as weak classifier selection, weight updating mechanisms, and final strong classifier combination. The implementation allows for hands-on verification of the algorithm's effectiveness through practical experimentation. The code structure includes essential AdaBoost elements: iterative weight adjustment using the formula w_i = w_i * exp(-α * y_i * h_t(x_i)), where α represents the classifier weight calculated as 0.5 * ln((1-error)/error). The program demonstrates how weak classifiers are combined through weighted majority voting to form a strong classifier. Through this implementation, I gained deeper insights into AdaBoost's boosting mechanism and improved both my programming skills and algorithmic understanding. This program provides significant value for anyone interested in studying AdaBoost, offering a practical foundation for further machine learning algorithm development and customization.