SVM Gaussian Kernel resource

What is SVM Gaussian Kernel resource?

Support Vector Machines (SVMs) are a popular type of machine learning algorithm used for classification and regression analysis. In SVM, a kernel function is used to transform the input data into a higher-dimensional feature space where the data becomes more separable. One of the most commonly used kernel functions is the Gaussian kernel (also known as the radial basis function or RBF kernel). The kernel function computes the distance between the input data and the support vector using a Gaussian (normal) distribution. The resulting distance is then used as a weight to determine the importance of the input data in the classification process.

Reference Links:

• A Tutorial on Support Vector Machines
• SVM and Kernel SVM
• Major Kernel Functions in Support Vector Machine (SVM)
• Major Kernel Functions in Support Vector Machine
• SVM with Gaussian Kernel & Visualizing the Support Vectors