Support Vector Machines
Support Vector Machines (SVMs) are a type of machine learning algorithm used for both classification and regression analysis. SVMs use a kernel trick to transform data into higher dimensions, where they can find the optimal boundary between classes or predict continuous values with minimal error. In this hierarchy, SVMs fall under Machine Learning Methods, which is itself a subcategory of Artificial Intelligence Algorithms in the broader field of Artificial Intelligence. As a result, SVMs are deeply connected to concepts such as pattern recognition and data modeling, which are essential components of Technology as a whole. SVMs represent one of many techniques available for solving complex machine learning problems, and they have been widely adopted across various industries due to their robustness and versatility.