Tukivektorien

Tukivektorien, often translated as support vectors, are a fundamental concept in Support Vector Machines (SVMs), a powerful class of supervised learning models used for classification and regression. In a classification task, tukivektorien are the data points from the training set that are closest to the decision boundary. These specific points play a crucial role in defining the hyperplane that separates different classes.

The core idea behind SVMs is to find the hyperplane that has the largest margin, meaning the greatest distance, between itself and the nearest data points of any class. These nearest points are precisely the tukivektorien. If these tukivektorien were removed or shifted, the position and orientation of the optimal hyperplane would change. Conversely, data points far from the decision boundary have little to no influence on its placement.

The number of tukivektorien is usually much smaller than the total number of training samples. This characteristic makes SVMs computationally efficient, especially when dealing with large datasets, as the model's complexity depends on the number of tukivektorien, not the entire training set. This property also contributes to the good generalization ability of SVMs, as they focus on the most informative data points for decision-making. Understanding tukivektorien is key to comprehending how SVMs learn and make predictions.