Explore MATLAB source code curated for "弱分类器" with clean implementations, documentation, and examples.
Adaboost source code - Adaboost is an iterative algorithm that trains multiple classifiers (weak learners) on the same training dataset and combines them to form a more powerful final classifier (strong learner). The algorithm implementation typically involves weighted training instances and sequential classifier training with error-based weight adjustments.
We have significantly enhanced the face detection algorithm by leveraging cumulative probability distribution points as threshold parameters in weak classifiers, improving classification efficiency through optimized histogram analysis.
Implementation of Adaboost algorithm for video tracking with automatic weak classifier updates. The system requires manual object definition by users (no automated recognition). The tracker handles diverse object types without constraints on size or shape assumptions, utilizing adaptive feature selection and classifier weighting mechanisms.
Implementation of Adaboost algorithm combining weak classifiers into strong classifiers with visualization and analysis of sample size impact on performance
Adaboost Algorithm with MATLAB Implementation - An iterative machine learning method that trains multiple weak classifiers on the same dataset and combines them into a powerful ensemble classifier through weighted voting mechanisms.
Implementation of the Adaboost algorithm that combines multiple weak classifiers to form a robust strong classifier through iterative weight adjustment and weighted voting mechanisms.
A Concise Explanation of the Adaboost Algorithm with Implementation Insights
A fundamental AdaBoost algorithm implementation source code in MATLAB with comprehensive technical explanations
AdaBoost: A Comprehensive Overview of the Classic Ensemble Learning Algorithm and Its Code Implementation