Explore MATLAB source code curated for "adaboost算法" with clean implementations, documentation, and examples.
This package provides a MATLAB implementation of the renowned AdaBoost algorithm, featuring enhanced threshold optimization to achieve superior classification performance through improved weak classifier selection and weighting mechanisms.
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 an AdaBoost-based linear threshold classifier in MATLAB - Combining weak classifiers to create a strong classifier through iterative weight adjustment
This program implements gesture recognition using the ADABOOST algorithm in MATLAB environment, featuring ensemble learning with weak classifier combination and performance optimization through weighted error minimization.
Modified MATLAB source code for license plate localization using Adaboost algorithm (updated September 10). This implementation includes integral image calculation, Haar-like feature generation, and enhanced feature selection mechanisms. Suitable for researchers studying machine learning applications in computer vision.
A MATLAB implementation utilizing the AdaBoost algorithm for face classification, with the JAFFE facial database serving as the test dataset. The system identifies input faces by calculating similarity scores between the input image and all prototype faces in the database, then ranking the results to determine identity. Key implementation steps include: 1. Model Training: Click the [Train] button to build the recognition model using 15 face images per subject from JAFFE database 2. Test Image Selection: Click [Open] to select a face image from JAFFE for testing 3. Recognition: Click [Identify] to automatically classify the input face and display results
AdaBoost Algorithm - One of the Fastest Classification Methods with MATLAB Source Code Implementation and Detailed Technical Explanations
Creating base Support Vector Machine classifiers through AdaBoost algorithm and integrating recognition results using simple voting method. Includes descriptions of SVM toolbox and AdaBoost algorithm workflow with code implementation insights.
A practical MATLAB program implementing the AdaBoost algorithm, featuring fundamental code structures and implementation approaches for machine learning applications
Face detection implementation based on AdaBoost algorithm using the original source code developed by Viola et al. This classic approach features cascade classifier structure with Haar-like features and integral image optimization, providing excellent reference value for beginners in computer vision.