stuðningsvigrar

stuðningsvigrar is an Icelandic term that translates to "support vectors" in English. It is a fundamental concept in the field of machine learning, particularly within the context of Support Vector Machines (SVMs). SVMs are a type of supervised learning model used for classification and regression tasks. The core idea behind SVMs is to find an optimal hyperplane that best separates data points belonging to different classes in a high-dimensional space.

The stuðningsvigrar are the data points from the training set that lie closest to the hyperplane. These are the critical points that determine the position and orientation of the hyperplane. If these stuðningsvigrar were removed, the hyperplane would shift or change. Conversely, points that are far away from the hyperplane do not influence its position and are therefore not considered stuðningsvigrar.

The margin is the space between the hyperplane and the nearest data points, which are the stuðningsvigrar. SVMs aim to maximize this margin, leading to better generalization performance on unseen data. The number of stuðningsvigrar is often much smaller than the total number of training samples, making SVMs computationally efficient in some cases, especially when dealing with large datasets where only a subset of the data is relevant for defining the decision boundary. The concept of stuðningsvigrar is central to understanding how SVMs work and their effectiveness in various machine learning applications.