Скачать или смотреть Support Vector Machine (SVM) – Kernel functions - Part 5 | Machine Learning | Data Science | AI

A Support Vector Machine (SVM) is a type of supervised learning algorithm used for classification and regression analysis. In SVM, the kernel function plays an important role in mapping the input space into a higher dimensional feature space, where it becomes easier to separate the classes using a linear boundary.

The kernel function is a mathematical function that takes two inputs, usually the two vectors that represent the input data points, and outputs a scalar value that measures the similarity or distance between the two points in the transformed feature space. There are several types of kernel functions available for SVM which are explained in this video.

For Learning about key concepts of SVM, check SVM Part1:    • Support Vector Machine (SVM) - Basics - Pa...  

For Learning about maths behind SVM, check SVM Part2:    • Support Vector Machine (SVM) – Maths behin...  

For Learning about maths behind SVM, check SVM Part3:    • Support Vector Machine (SVM) – Maths behin...  

For Learning about SVM Kernels, check SVM Part4:    • Support Vector Machine (SVM) – Kernel func...  

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