SVM Source Code Implementation in MATLAB

This MATLAB-implemented SVM source code enables Support Vector Machine functionality for feature classification and extraction tasks. The program employs machine learning algorithms to construct classification models through training data, which can subsequently classify new data or extract features. Support Vector Machines represent an effective machine learning methodology applicable across various domains including image recognition, speech processing, and natural language processing. The implementation typically involves key MATLAB functions such as svmtrain for model creation and svmclassify for prediction tasks. The code handles kernel functions (linear, polynomial, RBF) to transform input data into higher-dimensional spaces for optimal hyperplane separation. Through SVM utilization, we can enhance understanding and utilization of data characteristics, thereby improving classification accuracy and feature extraction efficiency. The algorithm works by finding the maximum-margin hyperplane that best separates different classes in the feature space, with support vectors determining the decision boundary. Common implementation aspects include data normalization, parameter optimization (like box constraint and kernel settings), and cross-validation techniques to prevent overfitting. The code structure typically comprises data preprocessing, model training, validation, and prediction modules, ensuring robust performance across diverse datasets.