Tugivektorite

Tugivektorite, often translated as "support vectors," are a fundamental concept in the field of machine learning, particularly within the context of Support Vector Machines (SVMs). In essence, tugivektorite are the data points that lie closest to the decision boundary that separates different classes in a classification problem. These points are crucial because they are the most influential in defining the margin of separation for the SVM.

The goal of an SVM algorithm is to find the optimal hyperplane (or decision boundary) that maximizes the distance (or margin) between the classes. The tugivektorite are precisely the data points that determine the position and orientation of this hyperplane. If these tugivektorite were removed, the hyperplane would likely shift. Therefore, the SVM's performance is heavily reliant on these specific data points.

In a linear SVM, the tugivektorite are the data points that fall exactly on the margins of the hyperplane. For non-linear SVMs, which use kernel functions to map data into a higher-dimensional space, the concept remains the same: the tugivektorite are the data points that are most critical in defining the decision boundary in that transformed space. Understanding and identifying tugivektorite is key to comprehending how SVMs work and how they achieve their classification capabilities.