Machine Learning - Why do we need kernel in SVM | kernel in Support Vector Machine in Machine Learning by Mahesh Huddar

Why do we need kernel in SVM | kernel in Support Vector Machine in Machine Learning by Mahesh Huddar Kernel Trick Kernel trick means replacing the dot product in mapping functions with a kernel function. 𝒌(𝒙, 𝒚) = ∅(𝒙)·∅(𝒚) Similar to mapping functions, kernels help in mapping data from input space to higher-dimensional feature space with the least computations. Performing the kernel operation is much easier compared to mapping functions. This is illustrated in the following numerical example. Consider two data points (1, 2) and (3, 4) Apply a polynomial kernel 𝑘(𝑥, 𝑦) = (𝑥^𝑇 𝑦)^2 and show that it is equivalent to mapping function ∅ = (𝑥^2, 𝑦^2, √2 𝑥𝑦) The following concepts are discussed: ______________________________ Why do we need a kernel in SVM, Why do we need kernel in Support Vector Machine, kernel in SVM, kernel in Support Vector Machine, kernel trick in SVM, kernel trick in Support Vector Machine, mapping function in SVM, mapping function in Support Vector Machine quadratic kernel, linear kernel, homogeneous kernel, inhomogeneous kernel ******************************** 1. Blog / Website: https://www.vtupulse.com/ 2. Like Facebook Page: https://www.facebook.com/VTUPulse 3. Follow us on Instagram: https://www.instagram.com/vtupulse/ 4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates