Explore MATLAB source code curated for "SVM" with clean implementations, documentation, and examples.
While SVM is typically applied to binary classification, this code utilizes Chih-Jen Lin's SVM toolkit for multi-class classification. The implementation is tested on the UCI IRIS dataset, demonstrating excellent performance through one-vs-one strategy and parameter optimization techniques.
MATLAB source code for SVM-based multi-class classification implementing training algorithms including One-Against-All (OAA), One-Against-One (OAO), and BSVM2 quadratic programming algorithms
An article comparing SVM and AdaBoost algorithms, implementing both methods for target recognition with detailed performance evaluation and code implementation insights.
Kernel Methods and Support Vector Machines (SVM) represent fundamental approaches in pattern recognition, each forming a distinct discipline with robust theoretical foundations and practical implementations.
This implementation utilizes Dense SIFT features with Bag of Words modeling for image representation. After encoding images using BoW, we train an SVM classifier for categorization. The methodology employs both RBF kernel and a custom histogram intersection kernel. Experimental validation uses action images across 6 categories (60 images per class) with 40 training and 20 testing samples per class. Code implementation includes feature extraction, vocabulary construction, and kernel function optimization.
Parameter optimization, classification prediction, and regression prediction with information granulation, implemented through machine learning algorithms.
Neural Network Toolbox featuring SVM and NPA algorithms, including "one-against-all" and "one-against-one" classification methods with extensive practical examples and code demonstrations.
A MATLAB implementation of online SVM incremental learning leveraging the built-in SVMTrain function for model training with support for continuous parameter updates.
Extreme Learning Machine Outperforms SVM in Speed with 4 Practical Implementation Examples
SVM face recognition program source code includes s1-s40 face database, face.m as main recognition program, and load_database.m for data loading function. Note: LIBSVM toolbox functions require manual installation - first install compiler, then execute make.m for setup.