tugivektoritega

Tugivektoritega, a term that translates roughly to "support vectors" in English, is a fundamental concept in the field of machine learning, particularly within the framework of Support Vector Machines (SVMs). SVMs are supervised learning models used for both classification and regression tasks. The core idea behind tugivektoritega is to identify the data points that are most critical in defining the boundary between different classes or in fitting a regression line.

These tugivektoritega are the data points that lie closest to the decision boundary. In a classification problem, they are the points that are either on the margin or misclassified. They are "supportive" in the sense that they are essential for determining the position and orientation of the hyperplane that separates the classes. If these tugivektoritega were removed, the decision boundary would likely change. In contrast, data points far from the decision boundary have little to no influence on its placement and are therefore not considered tugivektoritega.

The algorithms used in SVMs aim to find the optimal hyperplane that maximizes the margin between the classes, and this maximization is solely dependent on the tugivektoritega. This focus on a subset of the training data makes SVMs computationally efficient, especially when dealing with large datasets, as only the tugivektoritega need to be stored and considered for prediction. The concept extends to regression, where tugivektoritega are points that lie closest to the regression line, influencing its slope and intercept.