Understanding Support Vector Machines (SVM): A Comprehensive Guide
Introduction Support Vector Machines (SVM) are powerful and versatile machine learning algorithms used for both classification and regression tasks. Developed in the 1960s and further refined in the 1990s, SVMs have proven to be a reliable and efficient tool for various applications, such as image classification, text categorization, and anomaly detection. In this blog post, we'll take an in-depth look at SVMs, exploring their principles, mathematical foundations, applications, and key considerations for implementation. What is an SVM? At its core, an SVM is a supervised machine learning algorithm that seeks to find the best possible decision boundary or hyperplane that separates data points belonging to different classes. It does this by maximizing the margin between the nearest data points of each class, which are known as support vectors. These support vectors play a critical role in defining the hyperplane and make SVMs particularly robust and resistant to overfitting. Ho
