Hard SVM

 Hard SVM Understanding Hard SVM: A Comprehensive Guide Introduction Support Vector Machines (SVM) are one of the most powerful and versatile machine learning algorithms used for classification and regression tasks. The Hard SVM, also known as the Hard Margin SVM, is a fundamental variant of SVM used for linearly separable data. This blog delves into the core concepts, mathematical foundations, and practical implementation of Hard SVM.  What is Hard SVM? Hard SVM is a type of SVM that assumes the data is perfectly linearly separable. This means there exists a hyperplane that can separate the data points of different classes without any errors. The goal of Hard SVM is to find the hyperplane that maximizes the margin between the two classes. The margin is defined as the distance between the hyperplane and the nearest data points from either class, known as support vectors. Mathematical Formulation Given a training dataset \((\mathbf{x}_i, y_i)\) where \(\mathbf{x}_i\) are the fe...