What is the advantage of using kernels in SVM compared to adding multiple dimensions to achieve linear separability?
Monday, 07 August 2023 by EITCA Academy
Support Vector Machines (SVMs) are powerful machine learning algorithms commonly used for classification and regression tasks. In SVM, the goal is to find a hyperplane that separates the data points into different classes. However, in some cases, the data may not be linearly separable, meaning that a single hyperplane cannot effectively classify the data. To
How do kernels allow us to handle complex data without explicitly increasing the dimensionality of the dataset?
Monday, 07 August 2023 by EITCA Academy
Kernels in machine learning, particularly in the context of support vector machines (SVMs), play a important role in handling complex data without explicitly increasing the dimensionality of the dataset. This ability is rooted in the mathematical concepts and algorithms underlying SVMs and their use of kernel functions. To understand how kernels achieve this, let's first