SVM IN REAL LIFE
Fig1(a) and (b) : Intro-image Introduction: Support Vector Machines (SVM) stand as stalwarts in the realm of machine learning, often revered for their versatility and robustness. Originally developed by Vladimir Vapnik and his colleagues in the 1990s, SVM has found its way into a plethora of real-world applications, solving intricate problems with finesse. In this exploration, we delve into the practical domains where SVM shines, illuminating its efficacy in diverse fields. What Is SVM: Before we embark on our journey through real-life applications, let’s briefly revisit the essence of Support Vector Machines. At its core, SVM is a supervised learning model used for classification and regression analysis. It works by finding the optimal hyperplane that best divides a dataset into classes, aiming to maximize the margin between the classes. This intrinsic quality makes SVM particularly adept at handling c...
