Explore MATLAB source code curated for "支持向量机" with clean implementations, documentation, and examples.
Application Background: The current standalone SVM exhibits limited recognition accuracy. This program employs genetic algorithms to optimize the SVM algorithm, enhancing its precision and predictive performance. Key Technology: GA-SVM optimization algorithm improves recognition accuracy and prediction reliability through parameter tuning and model adaptation.
MATLAB implementation of Support Vector Machines featuring both linear and nonlinear algorithms, complete with comprehensive documentation files explaining usage and underlying methodologies.
Key programs focusing on Support Vector Machine implementation, providing reference code with comprehensive algorithm explanations and practical applications.
A support vector machine (SVM) toolbox designed for MATLAB environments, with essential features for researchers and practitioners working in machine learning.
A straightforward MATLAB implementation using genetic algorithms to optimize SVM parameters, featuring clear code structure with key functions for crossover, mutation, and fitness evaluation - ideal for beginners studying computational intelligence techniques.
This program extracts HOG and LBP features from positive and negative sample images, trains a classifier using Support Vector Machines, and implements pedestrian detection. Implementation details include feature vector extraction algorithms, SVM training methodology, and sliding window detection techniques. Experimental results demonstrate effective pedestrian detection with robust performance.
A compact MATLAB-based Support Vector Machine implementation for predictive modeling using Least Squares (LS) optimization
High-quality MATLAB programs for Support Vector Machine (SVM) implementation, featuring practical examples, improved algorithms, and pre-processed gene datasets for SVM analysis - ideal for machine learning research and education.
A MATLAB-implemented face recognition system utilizing Support Vector Machine (SVM) algorithm, thoroughly debugged and proven effective for practical applications.
A faster computational approach for Support Vector Machines that enhances processing speed, featuring the SVM-demo algorithm implementation.