MATLAB Algorithm for Support Vector Data Description

This article presents a MATLAB algorithm implementation for Support Vector Data Description (SVDD), specifically designed for one-class classification tasks. The program efficiently handles data classification problems while saving users significant time and effort in implementation. The algorithm employs kernel methods to create a hypersphere around target class data points, minimizing the volume while encompassing most of the training samples. Key functions include data preprocessing, kernel parameter optimization, and decision boundary calculation using quadratic programming techniques. The implementation features straightforward usage - simply download and install the program, then follow the step-by-step instructions provided in the documentation. The code includes sample datasets and visualization tools to help users understand the classification results. We welcome researchers and practitioners to download and utilize this program, and we believe it will provide substantial convenience and assistance in your machine learning projects and research work.