MATLAB Code Implementations for Support Vector Machines (SVM)

Below are several MATLAB code implementations for Support Vector Machines (SVM), with specific descriptions as follows:

1. SVM Implementation Code 1: This code utilizes a Gaussian kernel (RBF kernel) function, optimizing model accuracy through parameter tuning. During the training process, the dataset is split into training and testing sets using cross-validation techniques. The implementation includes calculating classification accuracy on the test set and may involve grid search for optimal hyperparameter selection (C and gamma parameters).

2. SVM Implementation Code 2: This implementation employs a linear kernel function, suitable for linearly separable datasets. The code similarly evaluates model performance based on accuracy metrics from training and testing sets. Key aspects include implementing the linear decision boundary and potentially using optimization algorithms like quadratic programming for the dual problem formulation.

3. SVM Implementation Code 3: This code features a polynomial kernel function, capable of handling non-linearly separable datasets. By adjusting polynomial degree parameters and other hyperparameters, better classification performance can be achieved. The implementation may include feature scaling preprocessing and polynomial kernel computation using MATLAB's kernel functions or custom implementations.

The above provides detailed introductions to several SVM implementations in MATLAB, hoping to be helpful for your projects!