Multi-class Classification using Nonlinear Support Vector Machine Classifiers

Implementing nonlinear Support Vector Machine (SVM) classifiers for multi-class classification using MATLAB is a widely adopted machine learning approach. This method effectively addresses multi-class classification problems by leveraging nonlinear SVM models that can better capture complex relationships within datasets. During implementation, common machine learning algorithms such as Radial Basis Function (RBF) kernel can be employed for feature transformation, where the kernel trick maps input data into higher-dimensional spaces without explicit computation. The SVM algorithm then performs classification by finding optimal hyperplanes that maximize margin separation between classes. In MATLAB implementation, key functions like fitcsvm() can be utilized for binary SVM training, while multiclass extensions may involve strategies like one-vs-one or one-vs-all approaches. The RBF kernel parameters (gamma and C) can be optimized using cross-validation techniques to enhance classifier performance. This methodology significantly improves classification accuracy and effectively handles nonlinear data distributions. Therefore, implementing nonlinear SVM classifiers for multi-class classification in MATLAB presents a valuable approach for practical applications, particularly when dealing with complex, non-linearly separable datasets.