MATLAB Implementation of S4VM Algorithm for Semi-Supervised Learning

When working with semi-supervised learning, the S4VM (Safe Semi-Supervised Support Vector Machines) algorithm represents an excellent choice. This algorithm can be efficiently implemented using MATLAB, featuring robust functionality for testing across diverse datasets. The implementation includes core components such as label propagation mechanisms, margin maximization techniques, and safety constraints that prevent performance degradation from incorrectly labeled instances. If you wish to experiment with this algorithm, you can directly download the code and utilize the provided examples for hands-on practice. The code structure demonstrates key functions for data preprocessing, model training with both labeled and unlabeled data, and prediction evaluation metrics. This approach enables deeper understanding of semi-supervised learning principles and practical applications, thereby enhancing your ability to solve real-world problems effectively. The implementation includes configuration files for parameter tuning and visualization tools for result analysis.