Linear Threshold Classifier Using AdaBoost Algorithm

Implementation of a linear threshold classifier using AdaBoost algorithm in MATLAB! This algorithm enhances classifier performance by combining multiple weak classifiers. During each training iteration, AdaBoost assigns higher weights to misclassified samples, enabling subsequent weak classifiers to focus more effectively on these challenging instances. Through iterative refinement, AdaBoost progressively learns and constructs a robust strong classifier. The algorithm operates through key MATLAB implementation components: weight initialization, weak classifier training with weighted error calculation, and classifier combination using weighted voting. In each iteration, the algorithm: - Computes classification errors for current weak classifiers - Updates sample weights based on misclassification results - Generates new weak classifiers focusing on previously misclassified samples - Combines all weak classifiers with optimal weights to form the final strong classifier This machine learning approach is widely adopted in the field due to its capability to handle complex classification problems while achieving superior performance. The MATLAB implementation typically involves functions for data preprocessing, iterative weight updating, and classifier evaluation metrics to monitor performance improvement throughout the boosting process.