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Support Vector Machine: A Comprehensive Guide — Part1
Support Vector Machines (SVMs) are a type of supervised learning algorithm used for classification and regression analysis. SVMs are particularly useful when working with complex datasets with high-dimensional feature spaces, and have been successfully applied in a wide range of applications, including image classification, natural language processing, and bioinformatics.
To explain SVM I have divided this topic into 10 subtopics. I will cover only the first 5 subtopics in this article and will cover the rest in my next upcoming article.
- What is SVM?
- Equation of a Line.
- Distance of a point from a plane.
- Geometric Intuition behind SVM.
- Hard and Soft Margin.
- SVM Mathematical Intuition.
- Cost Function
- Support Vector Regression (SVR)
- SVM Kernels
- Type of SVM Kernels
What is SVM?
SVMs are based on the idea of finding the hyperplane that separates two classes in a class. In a two-dimensional feature space, the hyperplane is a line that separates the two classes. In higher dimensions, the hyperplane is a plane. The goal of SVMs is to find the…
