MATLAB Implementation of AdaBoost Algorithm
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This MATLAB implementation of AdaBoost enables vehicle training and recognition capabilities. AdaBoost is an ensemble learning algorithm that constructs a strong classifier by combining multiple weak classifiers. The implementation utilizes MATLAB's machine learning toolbox for efficient weak classifier weighting and iterative boosting. Key functions include adaptive weight updating for misclassified samples and confidence-weighted voting mechanism for final classification. This approach is applicable to various machine learning problems including image recognition and pattern analysis. MATLAB provides a convenient development environment with comprehensive tools for rapid and accurate vehicle recognition training and testing, featuring built-in functions for data preprocessing, classifier evaluation, and performance visualization.
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