Explore MATLAB source code curated for "SVM" with clean implementations, documentation, and examples.
MATLAB implementation for face detection combining YCgCr color space skin modeling with Support Vector Machine (SVM) classification. The code performs skin region extraction through color thresholding followed by machine learning-based verification.
Implementation of SVM data classification and regression analysis using various kernel functions including RBF, sigmoid, and polynomial kernels with code integration
The k-fold cross-validation function commonly used in MATLAB's SVM implementations is often missing from the libsvm package and requires manual integration for comprehensive model evaluation.
Classification and applications of SVM - featuring detailed practical examples with code implementation insights
This MATLAB source code implements face recognition using SVM algorithm, consisting of five main components: 1) ORL face database for training and testing, 2) OSU_SVM toolbox for function calls, 3) Main program with detailed comments, 4) Analysis report of results in Word format, and 5) Important usage notes. The implementation demonstrates practical SVM application for image classification tasks.
The Moga-SVM implementation consists of three primary documents: 1. moga (Multi-Objective Genetic Algorithm module) 2. nsga (Non-dominated Sorting Genetic Algorithm module). These components form the foundation for evolutionary optimization in SVM parameter tuning.
SVM program code for pattern recognition and classification, applicable to image feature processing with enhanced algorithm implementation details.
Comprehensive collection of classification algorithms including SVM, kNN, and Decision Trees with detailed documentation and code implementation insights
A facial recognition program implementing Principal Component Analysis (PCA) for feature extraction and Support Vector Machine (SVM) for classification - shared with the technical community!
LIBSVM is a simple, easy-to-use, and efficient software package developed by Professor Lin Chih-Jen and team at National Taiwan University for SVM-based pattern recognition and regression. It provides both precompiled Windows executables and source code for customization, cross-platform adaptation, and algorithm enhancement. The package simplifies parameter tuning with extensive default configurations that handle most practical scenarios while offering cross-validation capabilities. It supports C-SVM, ν-SVM, ε-SVR, ν-SVR models and multi-class classification using one-vs-one strategy, with optimized implementations for large-scale datasets.