About this document
Understanding Kernels in SVM by raja2017pillai is a document available to read on EtoBox.
Support Vector Machines (SVMs) utilize the Kernel Trick to handle non-linear data by mapping it into a higher-dimensional space where it can be linearly separated. Various kernel functions, such as Linear, Polynomial, Radial Basis Function (RBF), Gaussian, and Sigmoid, cater to different data distributions and complexities. This approach enhances SVM
- Author
- raja2017pillai
- Language
- EN