Explore MATLAB source code curated for "SVM" with clean implementations, documentation, and examples.
Complete SVM source code implementation in MATLAB, including practical exercises for applying SVM algorithms to classification and regression problems, featuring hyperplane optimization and kernel function demonstrations.
MATLAB-based SVM implementation for small sample analysis with applications in remote sensing image classification, featuring customizable kernel functions and parameter optimization
Kernel Principal Component Analysis using Support Vector Machines, practical implementation with complete code suitable for thesis projects and academic research
A road detection algorithm based on Support Vector Machine (SVM) designed for high-resolution remote sensing imagery, featuring machine learning-based classification implementation.
A script file demonstrating the grid search method for identifying optimal parameters in Support Vector Machine (SVM) classification problems, including implementation steps and code references.
Implementation of pedestrian detection combining HOG features, LBP features, and SVM classifier with code-level explanations
Well-organized pattern recognition source code implementing LLE (Locally Linear Embedding) and SVM (Support Vector Machine) methods, including SVMFWD functionality - thoroughly tested and error-free implementation ready for download
Implementation of EEG signal classification through Support Vector Machine algorithm with preprocessing, feature extraction, and model training
This paper demonstrates the application of Support Vector Machine (SVM) as a robust foundation for improving k-nearest neighbor (kNN) classifiers. We introduce Discriminant Analysis via Support Vectors (SVDA), a novel multi-class dimensionality reduction technique that leverages SVM principles. The implementation involves using only support vectors to compute transformation matrices, reducing computational overhead for kernel-based feature extraction. Our methodology extends to non-linear versions through kernel mapping, achieving improved recognition performance in experimental validations across standard datasets.
Extraction of Gabor features from images, an effective texture representation method, combined with SVM (Support Vector Machine) for robust image classification